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The Integrated Regional Model Project. Vision Phase

FINAL REPORT

Denver Regional Council of Governments 4500 Cherry Creek Drive South

Suite 800 Denver, CO 80246-1531

303-451-1000 www.drcog.org

The Integrated Regional Model Project is a joint effort led by

Denver Regional Council of Governments 4500 Cherry Creek Drive South Suite 800 Denver, CO 80246-1531 303.451.1000 in partnership with Denver Regional Transit District 1600 Blake Street Denver, CO 80202 303.628.9000 Colorado Department of Transportation 4201 E. Arkansas Avenue Denver, CO 80222 303.757.9011

This report was prepared by

PB Consult Inc.303 Second Street

Suite 700San Francisco, CA 94107

415.243.4601

and

The Gallop Corporation451 Hungerford DriveRockville, MD 20850

301.838.0108

March 2005

TABLE OF CONTENTS

Executive Summary

Introduction

Evaluation of the DRCOG Model and Selected Travel Demand Modeling Systems

Desired Model Functionality and Potential Structural Enhancements

Design Alternatives for a Tour-Based Micro-simulation Travel Demand Model System

Data Inventory and Match to Structural Enhancements

Tour-Based Model Development Cost Estimate

Appendix. Travel Demand Modeling System Summaries

SECTION 1

EXECUTIVE SUMMARY

Denver Regional Council of Governments The Integrated Regional Model Project – Vision Phase

1. Executive Summary The Integrated Regional Model (IRM) Project Vision Phase was the second of three IRM project phases, whose purpose was to develop a broad “blueprint” for a next-generation modeling system for the Denver Region. The IRM project is a joint effort led by the Denver Regional Council of Governments in partnership with the Denver Regional Transit District and the Colorado Department of Transportation. It began in 2002 with the commencement of the first phase of the project, the Refresh Phase, and is scheduled to conclude in 2006, with the release of a new travel and socio-economic modeling system for the region, built during the project’s third Update Phase in accordance with the Vision Phase blueprint. In the mid-1990s, DRCOG began planning a multi-stage effort to upgrade the Denver region’s travel modeling capability. The planned effort was to include both a large-scale travel data acquisition project, and a model upgrade project to be built using that data. Experience with model development at DRCOG made it clear that the entire effort could require ten years or more to complete. The first project in this effort was the Travel Behavior Inventory (TBI), commenced in 1996 and completed in 2001 as the contracting phase of the IRM project got underway. The TBI project gathered extensive information on household and commercial travel, as well as on travelers crossing the region’s boundaries. The IRM Refresh Phase used the TBI data to update the current model with more recent travel statistics, leaving the existing model structure largely unchanged. The model was shifted to a modern software platform, among several other improvements. The goal and result of the Refresh Phase was the development of an improved modeling tool for use in the region while the next-generation model was being developed. The IRM Vision Phase was conducted in the wake of considerable research over the previous decade into improved socioeconomic and travel model structures. The climate in which regional travel demand modeling is conducted has changed considerably in recent years, with modeling systems required to respond to a much broader range of planning initiatives and questions than in the past. Research undertaken in response to these new requirements is now beginning to be applied in practical modeling settings, in part thanks to significant advances in computer hardware and software that make such improvements operationally feasible. The IRM Project was designed to bring those model advances into the planning process of the Denver metropolitan area. To effectively serve the regional transportation planning process, the Project Team felt that it was imperative that the new modeling system’s development be closely informed by its ultimate customers, which include elected officials and senior planning and engineering managers responsible for projects that at times rely heavily on model results. The IRM Vision Phase therefore was structured to incorporate the experience and knowledge of these model users. The first step in the Vision Phase, conducted in the summer of 2003, was a review of next-generation modeling efforts in the following North American and European cities: San Diego, Houston, Honolulu, Edmonton (Alberta, Canada), Portland (Oregon), Stockholm (Sweden), San Francisco, and Columbus, OH. It included an evaluation of the DRCOG travel demand model vis-à-vis the models currently implemented by Metropolitan Planning Organizations of a comparable size, and recommended a set of potential model enhancements for further consideration in subsequent stages of the Vision Phase. This review was conducted by PB Consult, which assisted the Project Team on the Vision Phase. The results of the model review were presented at an initial round of panel discussions conducted in the fall of 2003, aimed at assembling a set of planning, engineering and policy issues that the modeling system should better address. A technical panel of regional planners and engineers met first, followed by a policy panel of elected and appointed officials. The needs expressed by these panelists were discussed with a panel of modeling experts from around North America, which assisted the Project Team in developing a list of structural improvements to the model that could address these needs.

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Policy Review Panel

Bob Brady AQCC

Bob Sanderman Oakwood Homes

Bob Yuhnke Sierra Club

Cynthia Peterson AQCC

Dan Grunig Bicycle Colorado

Cliff Davidson North Front Range MPO

David Ruchman RTD Board of Directors

Doug Lempke AQCC Dept of Health

Frank Hempen Weld County

George Scheuernstuhl DRCOG

Greg Fulton Colorado Motor Carriers

Jeff May DRCOG

JoAnn Sorensen Clear Creek County

Karin McGowan DRCOG

Ken Lloyd RAQC

Ken Wolfe Elbert County

Liz Rao RTD

Margie Perkins APCD

Nancy Sharpe Greenwood Village

Peggy Catlin Colorado DOT

Rich Wobbekind CU Boulder

Rick Sheehan Jefferson County

Rob McDonald Pikes Peak Area Council

Steve Hogan NW Pkwy Authority

Theresa Donahue RAQC

Tom Clark Denver Metro Network

Will Toor City of Boulder

Technical Review Panel

Bart Benthul PPACG

Mac Callison City of Aurora

Cindy Christiansen Downtown Denver, Inc.

Lee Cryer RTD

Gerry Dilley RAQC

Andy Goetz University of Denver

Andy Gomez NFRMPO

Phil Greenwald City of Longmont

Pam Hutton, CDOT Region 1

Bruce Janson University of Colorado

William Johnson CDOT DTD

Lizzie Kemp CDOT DTD

Jake Kononov CDOT Region 6

Bob Manwaring City of Arvada

Mark Najarian City & County of Denver

Elena Nunez Environment Colorado

Gene Putman City of Thornton

Juan Robles CDOT DTD

Randall Rutsch City of Boulder

Sam Sager Colorado Environmental Coalition

Patty Silverstein Development Research

Brook Svoboda City of Blackhawk

Matthew Thornton NREL

Jim Townsend Jefferson County

Jeff Watson Douglas County

Bryan Weimer Arapahoe County

Modeling Expert Panel

Keith Lawton Portland METRO

Michael Replogle Environmental Defense

Keith Killough KLK Consulting

Frank Spielberg BMI-SG

Eric Miller University of Toronto

Michael Morris North-Central Texas COG

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During a second round of panel meetings, conducted in the spring of 2004, the Technical and Policy Panels developed a priority ranking of the model needs they provided in the first round. Subsequently, PB Consult developed design alternatives for a new travel demand modeling system that would address the policy and planning needs put forth by the panels. The prioritized needs list and model design alternatives were presented at the second and final modeling expert panel meeting, whose outcome was a specific set of recommendations for structural model improvements to address these needs. The top ten issues, in rank order are presented below, followed by the Modeling Expert Panel’s recommendations to address them.

Panel-Ranked Model Issues/Capabilities

1. Effects of development patterns on travel behavior

2. Sensitivity to price and behavioral changes

3. Effects of transportation system and system condition

4. Need for improved validity and reliability

5. Ability to evaluate policy initiatives

6. Better analysis of freight movement

7. Ability to show environmental effects

8. Modeling low-share alternatives

9. Better ability to evaluate effects on specific sub-groups

10. Reflect non-system policy changes (TDM, ITS)

Modeling Expert Panel Structural Improvement Recommendations

• The Panel agrees with the proposal to build the modeling system on a foundation of industry-standard data management techniques. This is fundamental and the panel encourages DRCOG to continue with it.

• On land use modeling, the panel encourages DRCOG to make progress where possible, but recognizes that data challenges may make this a longer-term effort. Land use modeling is critical, but may require more time than other model element improvements.

• Improving the freight model is important, and DRCOG should move forward using whatever data it has now. Build the best freight model possible now, and enhance it later. As it is difficult to do freight modeling properly at the MPO level, DRCOG is encouraged to work with CDOT in this effort.

• Implement travel demand modeling based on the activity/tour model, rather than the present separate trip-based approach. Implement a system similar to that being developed for the Mid-Ohio Regional Planning Commission (Columbus, OH).

• DRCOG lacks sufficient HOV and toll data to fully effectively model these two choices, so in the short run, look elsewhere for the necessary model elements, such as the experiences of I-15, SR 91 (Riverside County, CA), other places.

• DRCOG should proceed with its model upgrade efforts on its own and not wait for others. But DRCOG should start talking to other regions, to build a coalition that could cooperate on model improvement.

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• Move forward in parallel at feasible speeds on transportation, land use, and freight modeling simultaneously.

• DRCOG should develop pedestrian environment factors, and move forward on this issue. Look into using GIS to determine what data we can quickly derive.

• Current best practice does not address the effectiveness of most TDM or ITS measures. However, tour-based modeling puts you in a position to do a better job of it in the future. Start by building the data in your information system, then worry about modeling it later.

• Expand the model boundary to include the entire economic and air quality area.

Finally, PB Consult staff developed a detailed estimate of the staff and/or consultant time necessary to implement the Modeling Expert Panel’s recommendations. Project team members are now in the process of developing a detailed Update Phase work plan, together with a plan for consultant services to complement in-house work. Consultant services are expected to be solicited in the spring of 2005, with the bulk of the technical project work commencing in the summer of 2005. In summary, the main product of the Vision Phase was a set of model structural improvement recommendations, developed to respond to the needs expressed by local planners, engineers, and policy-makers, which will guide the development of the new regional modeling system during the IRM Update phase.

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SECTION 2

INTRODUCTION


2. Introduction The Integrated Regional Model (IRM) Project Vision Phase was the second of three IRM project phases, whose purpose was to develop a broad “blueprint” for a next-generation modeling system for the Denver Region. The IRM project is a joint effort led by the Denver Regional Council of Governments in partnership with the Denver Regional Transit District and the Colorado Department of Transportation. It began in 2002 with the commencement of the first phase of the project, the Refresh Phase, and is scheduled to conclude in 2006, with the release of a new travel and socio-economic modeling system for the region, built during the project’s third Update Phase in accordance with the Vision Phase blueprint. In the mid-1990s, DRCOG began planning a multi-stage effort to upgrade the Denver Region travel modeling capability. The planned effort was to include both a large-scale travel data acquisition project, and a model upgrade project to be built using that data. Experience with model development at DRCOG made it clear that the entire effort could require ten years or more to complete. The first project in this effort was the Travel Behavior Inventory (TBI), commenced in 1996 and completed in 2001 as the contracting phase of the IRM project got underway. The TBI project gathered extensive information on household and commercial travel, as well as on travelers crossing the region’s boundaries. The IRM Refresh Phase used the TBI data to update the current model with more recent travel statistics, leaving the existing model structure largely unchanged. The model was shifted to a modern software platform, among several other improvements. The goal and result of the Refresh Phase was the development of an improved modeling tool for use in the region while the next-generation model is developed. The IRM Vision Phase was conducted in the wake of considerable research over the previous decade into improved socioeconomic and travel model structures. The climate in which regional travel demand modeling is conducted has changed considerably in recent years, with modeling systems required to respond to a much broader range of planning initiatives and questions than in the past. Research undertaken in response to these new requirements is now beginning to be applied in practical modeling settings, in part thanks to significant advances in computer hardware and software that make such improvements operationally feasible. The IRM Project was designed to bring those model advances into the planning process of the Denver metropolitan area. To effectively serve the regional transportation planning process, the project team felt that it was imperative that the new modeling system’s development be closely informed by its ultimate customers, which include elected officials and senior planning and engineering managers responsible for projects that at times rely heavily on model results. The IRM Vision Phase therefore was structured to incorporate the experience and knowledge of these model users. Two panels of model customers were convened: a policy panel composed of elected and appointed officials, and a planning panel composed of transportation planners and engineers from around the region. In addition, the Vision Phase sought the advice of a panel of modeling experts from the United States and Canada. This document is organized around the major stages of the Vision Phase. The first stage, conducted in the summer of 2003, was a review of next-generation modeling efforts in the following North American and European cities: San Diego, Houston, Honolulu, Edmonton (Alberta, Canada), Portland (Oregon), Stockholm (Sweden), San Francisco, and Columbus, OH. It included an evaluation of the DRCOG travel demand model vis-à-vis the models currently implemented by Metropolitan Planning Organizations in cities of size comparable to Denver. This review was conducted by PB Consult, which assisted the Project Team on the Vision Phase. The results of the model review were presented at an initial round of panel discussions aimed at assembling a set of planning, engineering and policy issues that the modeling system should better address, conducted in the fall of 2003. The outcome of this first stage is documented in Section 3 – Evaluation of the DRCOG Model and of Selected Travel Demand Models.

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Policy Review Panel

Bob Brady AQCC

Bob Sanderman Oakwood Homes

Bob Yuhnke Sierra Club

Cynthia Peterson AQCC

Dan Grunig Bicycle Colorado

Cliff Davidson North Front Range MPO

David Ruchman RTD Board of Directors

Doug Lempke AQCC Dept of Health

Frank Hempen Weld County

George Scheuernstuhl DRCOG

Greg Fulton Colorado Motor Carriers

Jeff May DRCOG

JoAnn Sorensen Clear Creek County

Karin McGowan DRCOG

Ken Lloyd RAQC

Ken Wolfe Elbert County

Liz Rao RTD

Margie Perkins APCD

Nancy Sharpe Greenwood Village

Peggy Catlin Colorado DOT

Rich Wobbekind CU Boulder

Rick Sheehan Jefferson County

Rob McDonald Pikes Peak Area Council

Steve Hogan NW Pkwy Authority

Theresa Donahue RAQC

Tom Clark Denver Metro Network

Will Toor City of Boulder

Technical Review Panel

Bart Benthul PPACG

Mac Callison City of Aurora

Cindy Christiansen Downtown Denver, Inc.

Lee Cryer RTD

Gerry Dilley RAQC

Andy Goetz University of Denver

Andy Gomez NFRMPO

Phil Greenwald City of Longmont

Pam Hutton, CDOT Region 1

Bruce Janson University of Colorado

William Johnson CDOT DTD

Lizzie Kemp CDOT DTD

Jake Kononov CDOT Region 6

Bob Manwaring City of Arvada

Mark Najarian City & County of Denver

Elena Nunez Environment Colorado

Gene Putman City of Thornton

Juan Robles CDOT DTD

Randall Rutsch City of Boulder

Sam Sager Colorado Environmental Coalition

Patty Silverstein Development Research

Brook Svoboda City of Blackhawk

Matthew Thornton NREL

Jim Townsend Jefferson County

Jeff Watson Douglas County

Bryan Weimer Arapahoe County

Modeling Expert Panel

Keith Lawton Portland METRO

Michael Replogle Environmental Defense

Keith Killough KLK Consulting

Frank Spielberg BMI-SG

Eric Miller University of Toronto

Michael Morris North-Central Texas COG

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While the Technical and Policy Panels produced an extensive list of desired modeling capabilities and needs, they were asked at a second meeting to prioritize this list into the top ten modeling needs in the region. PB Consult was then asked to recommend a set of potential model enhancements to address these identified needs. Section 4 – Desired Model Functionality and Potential Structural Enhancements - is therefore organized around these top ten needs: 1. Effects of development patterns on travel behavior

2. Sensitivity to price and behavioral changes

3. Effects of transportation system and system condition

4. Need for improved validity and reliability

5. Ability to evaluate policy initiatives

6. Better analysis of freight movement

7. Ability to show environmental effects

8. Modeling low-share alternatives

9. Better ability to evaluate effects on specific sub-groups

10. Reflect non-system policy changes (TDM, ITS)

Given that one of the recommendations was to adopt a tour-based modeling framework, which essentially implies developing an entire new set of models for the region, PB Consult provided a detailed proposal of alternative design features and staged development for the implementation of tour-based models at DRCOG. This proposal is presented in Section 5 – Designed Alternatives for a Tour-Based Micro-Simulation Travel Demand Model System. The entire set of potential model structural enhancements was presented to the Modeling Expert Panel, along with the needs expressed by the Policy and Technical Panels. The Expert Panel offered the following recommendations to the Project Team: • The Panel agrees with the proposal to build the modeling system on a foundation of industry-

standard data management techniques. This is fundamental and the panel encourages DRCOG to continue with it.

• On land use modeling, the panel encourages DRCOG to make progress where possible, but recognizes that data challenges may make this a longer-term effort. Land use modeling is critical, but may require more time than other model elements improvements.

• Improving the freight model is important, and DRCOG should move forward using whatever data it has now. Build the best freight model possible now, and enhance it later. As it is difficult to do freight modeling properly at the MPO level, DRCOG is encouraged to work with CDOT in this effort.

• Implement travel demand modeling based on the activity/tour model, rather than the present separate trip-based approach. Implement a system similar to that being developed for the Mid-Ohio Regional Planning Commission (Columbus, OH).

• DRCOG lacks sufficient HOV and toll data to fully effectively model these two choices, so in the short run, look elsewhere for the necessary model elements, such as the experiences of I-15, SR 91 (Riverside County, CA), other places.

• DRCOG should proceed with its model upgrade efforts on its own and not wait for others. But DRCOG should start talking to other regions, to build a coalition that could cooperate on model improvement.

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• Move forward in parallel at feasible speeds on transportation, land use, and freight modeling simultaneously.

• DRCOG should develop pedestrian environment factors, and move forward on this issue. Look into using GIS to determine what data we can quickly derive.

• Current best practice does not address effectiveness of most TDM or ITS measures. However, tour-based modeling puts you in a position to do a better job of it in the future. Start by building the data in your information system, then worry about modeling it later.

• Expand the model boundary to include the entire economic and air quality area.

The last two stages of the Vision Phase primarily dealt with the practicalities of implementing the panels’ recommendations. An inventory of data available for model development was put together by the Gallop Corporation, and several recommendations were made regarding additional data that may be required depending on the final model improvements implemented. This data catalog, review and recommendations are presented in Section 6 – Data Inventory and Match to Structural Enhancements. Finally, PB Consult developed a detailed estimate of the staff and/or consultant time necessary to implement the Modeling Expert Panel’s recommendations. This cost estimated, expressed in labor hours, is presented in Section 7 – Tour-Based Model Development Cost Estimate. In summary, the main product of the Vision Phase was a set of model structural improvement recommendations, developed to respond to the needs expressed by local planners, engineers, and policy-makers, which will guide the development of the new regional modeling system during the IRM Update phase.

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SECTION 3

EVALUATION OF THE DRCOG MODEL

AND OF SELECTED TRAVEL DEMAND MODELING SYSTEMS


3. Evaluation of the DRCOG Model and Selected Travel Demand Modeling Systems

The Vision Phase of the IRM Project started with two concurrent but separate activities: establishing the desired capabilities of the Denver regional transportation model, and reviewing the attributes of the existing advanced travel demand models in general and of the current DRCOG model in particular. This section presents a summary evaluation of the characteristics of various advance-practice and state-of-the-art travel demand models, intended to serve as a benchmark state-of-the-practice modeling capabilities that could be implemented in Denver. It is followed by an evaluation of the DRCOG travel demand model vis-à-vis the characteristics of models now implemented by Metropolitan Planning Organizations (MPOs) in cities comparable in size to Denver. The purpose of these evaluations is to establish the state of the DRCOG model and document areas where the model can be improved to better address the region’s policy and planning needs, given the state of the practice. The travel demand models inventoried to serve as benchmark models for this comparison are:

• Edmonton: an advanced four-step model with consideration of journeys • Honolulu: an implementation of UrbanSim in a four-step model • Houston: an advanced four-step model with a powerful application • Columbus, Ohio: a tour-based micro-simulation model with explicit household interactions • San Francisco: a tour-based micro-simulation model • Portland, Oregon: advanced four-step and tour-based micro-simulation models • San Diego: four-step model with advanced data management system • Stockholm: a European advanced four step model

The first part of this section discusses the general characteristics of a best-practice model, using the inventoried models, when relevant, to show how these characteristics result in particular structural model forms. The second part discusses each of the main components of the DRCOG model with particular emphasis on the model strengths and weaknesses given best-practice model characteristics. The third and final part is a comparative evaluation of all the models herein discussed, in tabular form. More detailed descriptions of each of the inventoried models are available in the Appendix.

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3.1. Structural Attributes of Best Practice Travel Demand Models The DRCOG model is a state-of-the-practice, four-step travel model, comparable to models used in cities of similar size around the nation. Best practice model elements described below are designed to address some of the known shortcomings of current four-step model practice. The following sections describe the emerging best practice model elements, and contrast them with the present, almost universally used four-step approach.

Models Travel Decisions in Disaggregate Fashion

Arguably one of the most important attributes of a travel demand model is its ability to model travel decisions at the level at which they are made, be it the individual or the household. For model design and estimation, this level of disaggregation allows taking advantage of causal relationships between personal and household characteristics and the choices people make to travel. Furthermore, maintaining a fine level of disaggregation throughout the model application better represents the idiosyncrasies of the travel market, resulting in more accurate forecasts of travel behavior. At the same time, it allows summarizing and tabulating the model output for the population subgroups that define the level of model disaggregation. Standard practice in trip-based travel demand forecasting has been to apply discrete choice models in an aggregate fashion. This has two related disadvantages. First, a fine level of travel market disaggregation necessarily means maintaining multiple trip tables throughout the model chain, which greatly increases the cost of running the model and storing the model results. Second, the forecasts may suffer from aggregation bias because zonal averages need to be used in lieu of actual household or individual values. The most advance practice is to model all travel decisions at the individual level or household level, both in model estimation and in model application. The best examples of this practice are the tour-based micro-simulation models (Columbus, San Francisco and Portland). In terms of model estimation, it has long been recognized that most travel decisions are best represented as discrete choices from a set of well-defined alternatives. This has been standard practice for mode choice for some time, and it is becoming standard practice for trip distribution as well. The Columbus model goes several steps forward, by modeling not just these but all decisions as discrete choices made by each person, sometimes alone but sometimes jointly and/or conditional on the decisions of other household members. However, discrete choice trip generation models can be implemented within a four-step framework, as exemplified by the Edmonton model. The micro-simulation models instead apply all models at the household level. The Portland, San Francisco, and Columbus models, for example, forecast the travel decisions of each person in the population, so that the outcome of the model (prior to trip assignment) looks similar to a travel survey; i.e., for each person in the population the model forecasts a sequence of activities, their schedule and location, and the mode(s) used to access the various activities. This output can then be manipulated and summarized in the same way that a sample activity diary is processed, and in particular it is not defined nor constrained by the spatial aggregation (i.e., the traffic analysis zones) used by the model. Current four-step techniques model travel at an aggregate, i.e., zonal level. Moreover, they make limited use of travel market segmentation, particularly in trip distribution and mode choice.

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Maintains Intra-Household Linkages in All Travel Decisions

It is well known that people make travel decisions not in isolation but as members of a household. In fact, interactions with other family members, whether coordinating the use of household vehicles, sharing household responsibilities, or other joint activities, often affects and may in some cases largely determine people’s travel behavior. Thus, models that ignore such linkages misstate people’s responses to changes in the supply of transportation or in travel demand policies, and in particular are not able to realistically capture the effect of HOV policies in the formation of intra-household carpools. So far the only models that have proved capable of explicitly accounting for intra-household linkages are the tour-based micro-simulation models. The best example among the inventoried models is the Columbus model. This model explicitly accounts for joint maintenance and discretionary activities (and travel) among household members. The model also explicitly represents maintenance activities as ‘allocatable’ activities, i.e., activities that are generated (required) at the household level but performed by (allocated to) a household member, with full knowledge of this person’s and other household members’ mandatory activity choices. Current four-step models do not maintain intra-household linkages, neither within nor across travel decisions.

Explicitly Considers Relevant Individual and Household Characteristics

There are several characteristics of the individual and of the household that are proven powerful explanatory variables for all travel decisions. Such characteristics should be explicitly accounted for in a best practice model, whether as travel market stratification variables or as explanatory variables in the decision models. Experience suggests that the most relevant household characteristics are auto ownership or car sufficiency, household size, household income and labor force participation. Lifecycle stage is typically signaled by a combination of variables: age of head of household, number of children, age of youngest child. At the individual level, relevant characteristics are age, gender, occupation(s), whether the person has a driver’s license and how many hours per week they work outside of the home. In a four-step model, differences among household types are primarily accounted for through the use of market segmentation. Unfortunately the level of market segmentation, particularly in trip distribution and mode choice, is dictated by the need to produce travel estimates in a reasonable amount of time and using a reasonable amount of resources. However, for a large metropolitan area such as Denver, longer more costly runs are justified given the gain in prediction power that adequate segmentation affords. In Houston, for example, auto ownership and time of day (peak/off-peak) segmentation is maintained for the nine trip purposes through mode choice, and the home-based work models are further segmented by income and household size. The use of market segmentation, when the segmenting variables are chosen judiciously, can greatly reduce aggregation error. Nonetheless, fundamentally a trip-based model is an aggregate model. This means that demand elasticities with respect to household characteristics that enter the model (in application) as zonal averages are likely to suffer from high aggregation errors. In an activity or tour-based model, individual and household characteristics are pervasive in most decisions models, that is, they are explicitly forecasted at the individual level and used as explanatory variables. These characteristics also inform the choice of person-types on which the model is based. Because the models are estimated and applied at the person level, no aggregation error due to the use of average zonal values is incurred.

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In addition to increasing the explanatory power of the model, explicitly accounting for household characteristics can also help to give the model the ability to stratify the results by household subgroups, thus helping the analyst better assess the impacts of growth and travel demand policies on different population segments. Typical four-step models incorporate household characteristics only as market segmentation variables. Household income, size, auto ownership, number of workers and age of head of household are the most common segmentation variables. Most applications use several of these variables in trip generation, but only one of them, usually income or auto ownership, is carried throughout the entire model chain. However it is not uncommon to use two segmentation variables for home-based work trips.

Captures Relevant Short-Term and Long-Term Travel Choices and Household Characteristics

Evaluating the impact of travel behavior on system performance and vice-versa involves stepping through several long-term and short-term decisions, among them: residential location, labor force participation, auto ownership, purpose and frequency of tours or anchor activities, purpose and frequency of trips within a tour, activity location, activity scheduling, tour mode, trip mode, and route choice. In particular, long-term decisions, such as residential location or auto ownership condition and may even determine the outcome of shorter-term decisions, such as activity scheduling. Thus, when forecasting travel behavior many years into the future, it is critical that the travel demand model explicitly resolves these long term decisions using information about changes in the availability of jobs, the availability and quality of transit service, highway congestion, zoning ordinances, etc. Most of the inventoried travel demand models account in some fashion for shorter-term decisions, but long term decisions, particularly residential and job location decisions, are missing from all but three of these models (Honolulu, Portland and San Diego). The implementation of UrbanSim within the Honolulu four-step model provides a powerful tool to evaluate the impact of land development dynamics on travel behavior and vice-versa. In this implementation, the land use model outcome is fed to the travel model at five-year intervals, and similarly the travel demand model outcome (resulting from the land use outcome) is fed back to the land use model. There is no ‘equilibration’ of outcomes (among the two models) at any year; instead the model simulates a progression of effects. While the inventoried tour-based models do not include explicit employer location models, they do apply a work location choice model, either before or after auto ownership, which allows the model to consider the influence of work location choice on shorter-term daily travel decisions. The fundamental difference between this and the UrbanSim approach is that it assumes that the supply of jobs is invariant, while UrbanSim allows jobs to move spatially. Auto ownership is another household medium-term decision that tends to be overlooked, but less so than residential location. However, it is a critical step in a best practice model, because the availability of a car for any given person and/or trip affects not just the choice of mode, but also the choice of destination, the likelihood of intermediate stops between home and the primary destination, the likelihood of forming carpools, and the likelihood of performing joint activities, to mention some important decisions. Current four-step models often do not include medium (i.e. auto ownership) decisions (or use income as a proxy for them). The best current systems model some long-term decisions (such as residential and job location) through comparatively simple land use models combined with trip distribution models.

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Adequately Defines Feasible Choices for Each Travel Decision

Because travel behavior is modeled as a sequence of decisions, prior choices may determine the choice set available for a later decision. For example, if someone takes transit to work, then for all practical purposes it can be assumed that this person does not have a car available to make work-based trips. Four-step models cannot account for these linkages, because they are applied in an aggregate fashion. The ability to model travel decisions as conditional on prior choices adds considerable verisimilitude to the outcomes of the travel model, and should result in more accurate travel demand elasticities to price and other supply characteristics. Linkages between travel decisions are the purview of micro-simulation models. The Columbus model uses explicit linkages between tour and trip decisions and thus predicts consistent destination, mode and time of day choices for all trips within a tour. The San Francisco and Portland models maintain mode choice consistency, but do not attempt to explicitly model some characteristics of the intermediate stops. Trip-based models try to indirectly maintain some linkages by making judicious use of market segmentation techniques. For example, some models (such as Houston) define not just home-based trip purposes but also work-based trip purposes. However, current four-step models are trip-based, and as such do not maintain tour/trip linkages. Also, they often depict a limited set of modal options in the mode choice step.

Uses Adequate Accessibility Measures to Inform Travel-Related Decisions

In order to predict the effects of travel congestion on travel behavior, all travel decisions need to be informed by appropriate measures of accessibility. In regions where transit is expected to play a major role, it is not sufficient to use highway travel times as the measure of accessibility. For destination choice, accessibility can be measured by the mode choice logsum. This measure incorporates highway and transit accessibility, as well as information about the household and location-specific variables included in the mode choice model. It incorporates walk accessibility as well when non-motorized modes are included in the choice set. Recent experience suggests that logsums tend to overestimate trip length; however there are practical ways to overcome this problem should it present itself. All of the inventoried models except Honolulu and San Diego use mode choice as the location choice impedance or gravity model impedance. Trip generation type models (including tour frequency) and auto ownership models should also include measures of accessibility as explanatory variables. The Edmonton model uses logsums from the destination choice model to inform trip frequency choice. The Columbus daily activity pattern and some of the tour generation models include variables such as the number of jobs within 30 min. of transit or within a 30 min. walk. Similar variables are used in auto ownership models, for example in Houston. In order to produce effective measures of accessibility, all models need to use consistent highway and transit travel time estimates. To do so it is of paramount importance that the model chain includes feedback loops from trip assignment back to trip/tour generation, auto ownership, or even residential location if possible. It is also critical that the model runs to convergence, that is, that the travel time estimates used throughout the model chain are consistent with the final estimates produced by the model. The number of iterations required for convergence can be reduced by setting adequate initial congested speeds on the highway network, instead of using free-flow speeds. A related issue is the fineness of the time of day segmentation used in assignment, which determines the extent to which time of day differences in level of service inform travel decisions, and also the extent to

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which the model is able to capture peak spreading. Most models use at least two periods, peak and off-peak. In four-step models, it is typically not practical to use a finer segmentation because it greatly increases the number of trip tables to maintain. However some (though none among the inventoried models) use peak spreading models to distribute the ‘period’ demand to individual hours. In micro-simulation models, time-of-day segmentation can be as fine as is practical given that for each segment it is necessary to perform a full highway assignment. Thus for example, while the Columbus model maintains a one-hour resolution on its time-of-day choice models, for practical considerations only four assignments are routinely performed. The San Francisco and Portland models perform five assignments. One final important issue to be mentioned is that none of the inventoried models uses measures of transit crowding, and thus this inconvenience is not reflected in the accessibility logsums. They also do not use shadow pricing or similar techniques to constrain (or at least price) transit demand at stations where the parking capacity is exceeded.

Predicts Realistic Highway and Transit Level of Service

It is not sufficient for a best practice model to produce accurate traffic volumes and transit boardings. The model is also expected to estimate accurate highway speeds and reasonable measures of highway and transit level of service. These measures are important in the evaluation of user benefits and air quality and noise impacts. Accurate highway speed estimation is a function of the volume-delay functions and of the time of day segmentation. The standard BPR functions may understate speed degradation as volume reaches or exceeds capacity. In several models alternative parameters and functional forms are used; this continues to be an area of on-going research and experimentation. The fineness of the time of day segmentation determines the extent to which peak spreading, and thus lower speeds in shoulder periods, is captured by the model. It should be recognized, however, that a regional model at best is able to estimate average hourly speeds, which may be irrelevant when peak 15 min. averages or intersection queuing information are required. Other tools, such as traffic simulation models, need to be used in tandem with the regional model to obtain the desired information. Some of the lack of progress in estimating adequate speed-delay functions is due to lack of adequate observed speed or travel time databases. When available, point speed estimates tend to be limited to a few facilities and times of day. There are also practical issues when transferring such functions across regions, because their parameters may be network-specific. The richness (or lack of) of the highway network database, also limits the extent to which volume-delay functions can be tailored to accurately differentiate between various types of facilities. Typically links are classified according to function (freeways, expressways, arterials, collectors, etc.) and one volume-delay function use for each class. A richer database, including for example street width, presence and width of shoulders, signal spacing, type of intersection control, and pavement condition among other characteristics, can serve as the building block for developing and applying volume-delay functions that better represent average conditions for a group of links. In Houston, a more detailed classification of links is used in post-assignment than in assignment itself to produce speeds for use in air quality modeling. An aspect that tends to be overlooked in regional travel demand models is the estimation of realistic transit travel times as well as the use of other transit level of service variables in mode choice and accessibility calculations. With regards to travel time, the problem seems to be more an issue of insufficient resources devoted to transit calibration and validation than an inherent inability to properly represent transit travel times as a function of the underlying street network travel times. On the other hand, lack of progress in using other level of service measures appears to be due to practical issues

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related to forecasting such variables. This is another area where some amount of experimentation and additional research is required.

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3.2. Comparative Evaluation of the DRCOG Travel Demand Model This section discusses the strengths and weaknesses of each major component of the DRCOG model vis-à-vis ‘best-practice-model’ characteristics as previously described and the features of the inventoried models. The most salient features of these models and the DRCOG model are described in the table at the end of this section. A more detailed description of each model is available in the Appendix.

Land Use Model

DRCOG uses an allocation-type land use model, which begins with exogenous forecasts of region-wide growth in population, households, and income, and allocates the growth to each TAZ in the modeling system. The allocation is based on approximately 25 variables, including transportation-related variables such as congestion, and land use – related variables such as proximity to open space. The variables are used in a linear combination to produce a “score” for each TAZ, with the coefficients used to weight the variables determined through a “Delphi” process, with participation from planning and economics practitioners from throughout the region. This type of land use model is standard and accepted practice. In order to examine the impact of transportation improvements on land use in a more dynamic fashion, some regions (Portland and Oahu) have implemented land use models that simulate land development dynamics from the interplay of supply of developable land and demand for residential, commercial and industrial development. Among many other factors, these models use travel times and other measures of accessibility derived from the regional travel demand model in the land use simulation, and feed the estimated land allocation to the travel demand model to update the travel time and accessibility estimates. A typical application iterates between the land use and travel demand models at five-year intervals, over a total simulated period of 20 to 30 years. DRCOG is currently pursuing the implementation of this type of land use model.

Household Sub-Models

The DRCOG model segments the travel market by household size and income. The marginal distribution of households by income group for each TAZ is a model input. It is currently based on the 2000 Census distribution. The model then applies first a sub-model that gives the marginal distribution of households by size (five classes) as a function of zonal average household size. Then an iterative proportional fitting (IPF) procedure is applied to estimate the joint household distribution by size and income for each TAZ, using the regional distribution as the seed, and the marginal distributions as controls. In addition, the TAZ household distributions are required to add up to the regional household distribution. These sub-models are sound, even if demanding from the perspective of model inputs: they require estimating the distribution of households by income for each TAZ and they require estimating a joint regional distribution of households by income and size for each forecast year, as opposed to the more common marginal distributions. A standard upgrade to DRCOG’s current model, while still remaining in the trip-based structure, would be to implement an automobile ownership model, and use a distribution of households by auto ownership rather than income. However, the shift to an activity/tour-based model being explored in this project would provide greater benefits. Models similar to the one used by DRCOG to derive a distribution from average zonal values can be used to obtain marginal distributions by number of workers, and iterative proportional fitting techniques used to obtain n-dimensional joint distributions. Auto ownership is typically modeled as a discrete choice, and includes household characteristics and accessibility measures as explanatory variables. The importance of auto ownership (or car sufficiency)

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segmentation in later stages of the model cannot be overstated: significant differences have been found in average trip lengths and mode choice as a function of car availability.

Trip Generation Model

DRCOG uses cross-classification models for trip productions and attractions. In the production models the trip rates are stratified by household size and income. In the attraction models the trip rates are stratified by employment income groups (three classes) for home-based work attractions, and also include a term related to the number of households. For home and non-home based non-work models, trip rates per household and job (classified by major employment group) are used. A sub-model is used to estimate the distribution of jobs by income. In addition, the model flags certain activities as special generators using a well-defined methodology and uses site-specific trip rates or traffic counts in lieu of the general trip rates. Within a four-step model framework, the DRCOG trip generation models are state-of-the-practice models. A standard upgrade to these models, while remaining in the trip-based model structure, would be to explore whether a finer classification of trip purposes would yield significantly different trip rates. More importantly than the trip rates per se, is that a finer purpose segmentation would give separate consideration to travel markets that have shown to have unique trip destination and mode choice characteristics, such as home-based school and home-based shop trips. It has also become standard practice to subdivide non-home-based trips into trips based at work and other trips. However, the shift to an activity/tour-based model being explored in this project would provide greater benefits. It appears that several model updates have found that the production trip rates have increased over the years. Assuming that this is not a data-related issue (i.e., that better surveying techniques have resulted in fewer unreported trips), it can be concluded that the current variables used to segment the travel market are not adequately capturing trip generation behavior in the region over time. It would be interesting to include variables known to explain differences in trip rates, such as number of workers, auto ownership (which in turn would be informed by accessibility variables), and observe whether this helps to stabilize the rates over time. This project will explore this and more complex interactions at the household and tour/trip levels as part of the tour-based modeling design.

Trip Distribution Model

DRCOG uses a gravity model formulation to distribute trips. The home-based work trips are segmented by income. No income segmentation is used for the non-work trips. To reduce model size and operating time, all HBW trips are distributed with peak period impedances while all non-work trips are distributed with off-peak impedances. The models use composite highway travel time and distance impedance, where the travel time is weighted by the value of time and the distance weighted by the auto operating cost. Destination choice model structures being considered in this project offer several advantages over the gravity model typically used in regional models today. Destination choice models can allow for explicit consideration of household and area-specific variables that have been found to influence trip distribution. In the gravity model framework, such effects can be considered, at best, by segmenting the travel market and by using K-factors. A destination choice model allows explicit inclusion of socio-economic, geographic and political-boundary variables in the utility function. A formal statistical process can be used to estimate coefficients on those variables, which in turn provide some indication of the relative importance of non-impedance factors. Gravity models rely on accurate estimates of trip attractions; when the trip attraction models are weak (for example because of data quality), a destination choice formulation using size variables may be preferable. The gravity model assumes that friction factors,

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which largely determine trip lengths, do not vary over time. It tends to perform best when travel is oriented towards one central core. The income stratification in the DRCOG models is useful to connect low-income households with low-income jobs, etc., and results in somewhat longer duration trips for high income than low-income trips (about 16% longer). Lack of auto ownership consideration also is likely to overestimate the trip lengths of zero car households, an important transit market. Assuming that all work trips face peak conditions and all non-work trips face off-peak conditions is a common strategy in current models. However, distribution results also could be improved by disaggregating trip purposes into peak and off-peak components, so that trips more realistically respond to the travel conditions they actually face. Finally, DRCOG’s model employs only highway service conditions in its trip distribution, again a typical strategy in current models. Recent advances in trip distribution take into account the travel conditions of all modes by using mode choice logsums, or a combination of logsums and distance, in place of highway travel times, which can result in a distribution model that shows some elasticity with respect to transit service and pedestrian access.

Mode Choice Model

Evaluation of mode choice is an area with great opportunity for improvement over the existing DRCOG model, especially given the region’s interest in promoting transit and non-motorized travel, as well as the need to evaluate the effectiveness of HOV and highway toll policies. Limitations of the current model include:

• Representation of regional modal availability. The home-based work model does not include non-motorized travel or individual representation of the various transit modes available. The non-work models are simple binomial models that offer only two choices, auto and transit, supplemented by a post-processor that divides auto modes by occupancy level.

• The models do not differentiate between transit modes (local bus, express bus, light rail, commuter rail) in a nested structure. A nested structure’s ability to evaluate competition between transit modes is superior to the existing multinomial structure. For example, it is likely that considering walk-to-transit and drive-to-transit in the same nest as the auto options may overestimate modal cross-elasticities. A more appropriate form for the existing choice set may be a nested logit model.

• None of the models are stratified by car sufficiency or car ownership. These variables are typically better stratification variables than the existing model’s stratification by income.

• The models do not use any segmentation technique to model transit access, which may misrepresent walk accessibility.

• The home-based work model uses peak skims only while the non-work model uses off-peak skims. This ignores time-of-day differences within a given trip purpose in modal availability and usage.

Time of Day Model

The most recent version of the DRCOG model uses constant, invariant diurnal factors to distribute the daily trip tables into the time periods used for assignment. This methodology is consistent with state-of-the-practice for trip-based models, in that it does not attempt to model peak spreading over time. The alternative is to implement a departure time choice model, as was done in Edmonton (though not

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necessarily a simultaneous mode choice & departure time model). DRCOG currently is working with the FHWA to implement a time of day model.

Highway Assignment

The best methodology for assigning trips to the highway network continues to be multi-class user equilibrium assignment, as is used in the DRCOG model. Nevertheless some observations can be made about their specific application. First, it is unclear why truck trips are pre-loaded, other than a legacy from prior assignment packages. Truck trips should be loaded as a separate class but simultaneously with vehicle trips. Second, best practice is moving toward estimation of HOV and Toll options both in mode choice and in highway assignment. The most detailed example of this implementation among the inventoried models is the Houston model.

Transit Assignment

Standard practice has been to perform all-or-nothing peak and off-peak transit assignments, because no other options were available in commercial travel demand modeling packages. DRCOG now uses a multi-path, capacity unconstrained assignment procedure. However, with the change of software platform to TransCAD, DRCOG now has the option of implementing and experimenting with capacity-restraint transit assignment methods.



Table 3.1. Comparative Description of Selected Travel Demand Models. Denver Edmonton Oahu General Characterization

Four-step model Four-step model using discrete choice trip generation.

Four-step model with connected land use model (UrbanSim).

Principal Characteristics of Main Model Components

Land Use Model Allocation model developed in-house, that allocates regional growth control total to TAZs based on a variety of transportation and land use variables. Some behavioral characteristics.

Unknown UrbanSim. Simulates land use dynamics through the interplay between the supply of developable land and the residential and commercial demand for land. Addresses housing market clearance. Incorporates network accessibility measures at selected years in a multiple year forecast.

Trip Generation Cross-classification productions models stratified by household size and income. Attraction trip rates applied by income group and total households (work trips), employment type and households (non-work trips). Rigorous treatment of special generators.

Choice-based. Models 25 person trip markets, defined by 5 person-types, 5 activity-types and status of auto needed at work. Includes accessibility measures from the destination choice model.

Cross-classification with attraction models.

Trip Distribution Gravity. HBW stratified by income. Composite time and distance impedance. Peak travel times used for work trips, off-peak travel times used for non-work trips.

Choice-based, stratified by person trip market. Includes use of district factors and trip length scaling.

Choice-based. Uses travel time as the impedance measure. Stratified by auto ownership.

Mode Choice Two models: A simple diversion model to be used in roadway analysis where transit is not critical. A choice based, logit model for all other applications. The work trip is multinomial (five modes), stratified by income; the non-work models are binomial (auto/transit), not stratified. Does not consider non-motorized modes or Toll or HOV modes. Not stratified by time period. Uses a separate toll diversion model.

Choice-based, nested logit. Joint mode choice and time-of-day choice.

Choice-based, nested logit.

Time of Day Choice Diurnal factors, invariant. Choice-based (AM/PM/Off), (Crown/Shoulders) within peak periods. Nested with the mode choice.

Diurnal factors, invariant.

Trip Assignment Zone-based multi-class equilibrium assignment, but with pre-loaded truck trips. Hourly assignments, 10 time periods.

Zone-based. Hourly assignments (peak crown, peak shoulder and off peak)

Zone-based multi-class equilibrium assignment, three time periods (AM peak, PM peak, off-peak).



Comparative Description of Selected Travel Demand Models (continued) Houston San Diego Portland (Trip) General Characterization

Four-step model with comprehensive household segmentation and explicit treatment of Toll and HOV modes.

Four-step model, with first and second stage forecasts and land use allocation model.

Four-step model complemented with a land use model.


Land Use Model Allocation model. Forecasts regional growth at the county level.

Urban Development Model (UDM). Allocates employment, population, housing and income from the regional forecast to census blocks. Incorporates network travel times in the allocation.

Metroscope. Based on a simultaneous equation system that describes commercial and residential land supply and demand. Final allocation is decided among jurisdictions. Incorporates network accessibility measures at selected years in a multiple year forecast.

Trip Generation Cross-classification production and attraction work models. The latter were developed based on a work place survey and are stratified by employment type. Cross-classification non-work productions with non-stratified attraction models.

Trips per dwelling unit and trips per acre or employee by land use type, applied at census block level. Productions and attractions are balanced to a regional total independently developed.

Cross-classification, stratified by household size and workers. Models 8 trip purposes.

Trip Distribution Atomistic Gravity. Uses logsums as the impedance measure, standardized to a distance-based frequency distribution.

First and second stage models are gravity-type models. First stage impedance is off-peak travel times. Second stage impedance is a weighted average of peak travel time, off-peak travel time and distance.

Choice-based. All models are stratified by income and incorporate multimodal accessibility measures.

Mode Choice Choice-based, nested logit. Considers explicitly Toll and HOV modes, five primary transit modes and three transit access modes.

First stage: factors. Second stage: choice-based, nested logit models. Explicitly considers toll trips as a mode choice.

Choice-based. Models are multinomial logit. To avoid IIA problems, only one main transit mode per OD pair is allowed.

Time of Day Choice Diurnal factors, invariant. Diurnal factors, invariant. Diurnal factors, invariant. Trip Assignment Zone-based multi-class

equilibrium assignment. Zone-based. Separate AM peak, PM peak and Off peak assignments.

Zone-based multi-class equilibrium assignment. Uses S-shape VDFs.



Comparative Description of Selected Travel Demand Models (continued) Columbus San Francisco Portland (Tour) General Characterization

Tour-based micro-simulation with explicit household interactions.

Tour-based micro-simulation. Tour-based micro-simulation.


Land Use Model Unknown Unknown Trip Generation Choice-based

Varying decision unit (person, household, tour) depending on trip purpose. Considers individual, joint and conditional decisions. Seven person types, 12 purposes stratified into 3 main categories (mandatory, maintenance, discretionary).

Choice-based. Simultaneously predicts full day tour patterns (main tour purpose and number of trips in the tour) for each individual. Subsequent conditional models predict number of stops (if 2 or more).

Trip Distribution Choice-based. Decision unit for the primary tour destination is the home-based tour or a work-based sub-tour. Stratified by purpose, person type, individual vs. joint tours. Choices are TAZs subdivided by transit accessibility. Stop location is also choice-based, conditional on the primary tour destination, mode and TOD.

Choice-based. The workplace location decision is modeled prior to the full day pattern and auto ownership decisions. The non-work destination model is conditional on anchor location and full-day tour pattern.

The workplace location decision is modeled after the full day pattern and auto ownership decisions. Even finer zonal subdivision (approx. 10,000 zones), resulting in improved representation of activity opportunities and transit accessibility.

Mode Choice Choice-based, both main tour and individual trip models. Trip modes are conditional on the main tour mode.

Choice-based. Predicts main tour mode and trip modes conditional on the main tour mode. High level of zonal detail obviates need for walk accessibility to transit segmentation. Includes several measures of pedestrian friendliness environments.

Time of Day Choice Choice-based with one hour temporal resolution; functions as an activity scheduler.

Choice-based, with a full day subdivided into five time periods. Simultaneously predicts departure time from home on the first tour and departure time from the primary destination to return home. Departure time from intermediate stops allocated given tour departure times.

Trip Assignment Zone-based multi-class equilibrium assignment. Currently assigns four time periods but can be easily disaggregated up to 24 one hour periods.

Zone-based multi-class equilibrium assignment. Uses five time periods.



Comparative Description of Selected Travel Demand Models (continued) Denver Edmonton Oahu Disaggregate Modeling of Household Characteristics

Lifecycle stage No No No Household size Yes No Yes Labor force participation

No Yes Yes

Auto ownership No No Yes Income Yes No Yes Explicit Modeling of Household Interactions

No No No

Explicit Modeling of Tour-Trip Interactions

No No No

Addresses Location and Travel-Related Decisions

Home location No No Yes Job location No No Yes Auto ownership No No Yes Number and purpose of trips

Yes Yes Yes

Trip destination Yes Yes Yes Primary travel mode Yes Yes Yes Secondary travel modes

No No No

Trip chaining No No No Time of day No Yes

Resolution of 3 time periods, and one hour within the peak periods

No

Travel budgets No No No Route choice No Yes Yes Responds to Highway and Transit Congestion

Highway travel time feedback and convergence

Feeds peak and off-peak period speeds back to the trip distribution model. Convergence check involves comparing number of links with speed change over a set percentage.

Two-level feedback: a. back to mode choice b. back to trip generation

Highway and transit travel times fed back to distribution and mode choice. Convergence checked for highway links and trip table (district basis).

Incorporates travel time reliability

No No No

Sensitive to transit congestion/crowding

No No No



Comparative Description of Selected Travel Demand Models (continued) Houston San Diego Portland (Trip) Disaggregate Modeling of Household Characteristics

Lifecycle stage No No No Household size Trip generation only No Yes Labor force participation

Trip generation only No Yes

Auto ownership Yes, through mode choice No Yes Income Yes, some purposes through

mode choice Yes, mode choice. Yes

Explicit Modeling of Household Interactions

No No No


No No No


Home location No Yes Yes Job location No Yes No Auto ownership Yes No Yes Number and purpose of trips

Yes, highly stratified purpose segmentation to approximate a tour-type model.

Yes Yes

Trip destination Yes Yes Yes Primary travel mode Yes Yes Yes Secondary travel modes

No No No

Trip chaining No No No Time of day No No No Travel budgets No No No Route choice Yes Yes Yes Responds to Highway and Transit Congestion


Travel times fed back to trip generation until model convergence is reached.

Model is iterated only twice. Congested times are input to the second stage distribution and mode choice models.

Travel times are fed back all the way to the land use model


No No No


No No No. Incorporates shadow price for PNR lots, which can be interpreted as a measure of demand.



Comparative Description of Selected Travel Demand Models (continued) Columbus San Francisco Portland (Tour) Disaggregate Modeling of Household Characteristics

Lifecycle stage No No Household size Yes Yes Labor force participation

Yes Yes

Auto ownership Yes Yes Income Yes Yes Explicit Modeling of Household Interactions

Yes No


Yes Yes


Home location No, total TAZ households per target year is a model input

No

Job location No, total TAZ labor force per target year is a model input

No

Auto ownership Yes Yes Number and purpose of trips

Yes Yes, though purpose of intermediate stops not explicitly modeled.

Trip destination Yes Yes Primary travel mode Yes Yes Secondary travel modes

Yes Yes

Trip chaining Yes Yes Time of day Yes

Resolution of one hour for all activities

Yes Resolution of five time periods for the tour trip ends

Travel budgets Yes No Route choice Yes Yes Responds to Highway and Transit Congestion


Specific strategy still not defined. Desirable to feed back to tour generation, but this complicates the comparison and evaluation of alternatives, particularly within the SUMMIT framework.

No feedback. Congested travel times are obtained by assigning trip tables from the MTC model (which is run with feedback).


No No, however issue was addressed in a stated-preference survey.


No No, however issue was addressed in a stated-preference survey.



Comparative Description of Selected Travel Demand Models (continued) Denver Edmonton Oahu Treatment of Low Share Alternatives

No explicit treatment of non-motorized modes. Shared-ride trips are assigned to HOV lanes in assignment only. Includes a toll way diversion model to address E-470.

Detailed walk and bicycle networks. Explicit consideration of walk and bike modes in mode choice. Bike users’ survey. Pedestrian counts. Non-motorized trips assigned to network.

No explicit treatment of low share modes. Does not include non-motorized modes

Explicit Consideration of Price-related Issues

Differentiates between perceived and actual costs (e.g. transit passes, pre-tax options, employer-based parking)

Mode-related costs are allocated to individuals, not households.

No No

Differentiates between sunk and marginal costs

No No

Aggregation bias Subject to bias due to use of average zonal parking cost. Explicit treatment of parking discounts for higher-income workers. Boarding fare considers average discount due to use of passes.

Subject to bias due to use of average zonal parking cost. No explicit treatment of transit or parking discounts.


Available price elasticities

Mode choice cost coefficients transferred from other areas.

Travel cost Land price, parking cost, vehicle operating cost

Ability to Address Policy Issues

Provides confidence bands, not just point estimates for all kinds of model output (boardings, traffic, emissions estimates)

Only through sensitivity analyses Only through sensitivity analyses Only through sensitivity analyses

Is sensitive to development patterns and development costs

Only through zonal forecasts of employment, school enrollment and population


Yes, in addition to simultaneous modeling of land supply and demand, it can incorporate zoning and development ‘events’.

Sensitive to shifting demographics over time

Indirectly, by controlling regional household type totals

Possible for person types modeled in trip generation (students / adults / seniors).


Able to evaluate impacts to particular demographic/ethnic groups (seniors, minorities)

No Seniors only No



Comparative Description of Selected Travel Demand Models (continued) Houston San Diego Portland (Trip) Treatment of Low Share Alternatives

Surveys on Toll and HOV users are available and were used to estimate and calibrate the mode choice models. Includes explicit consideration of walk and bike modes in mode choice, but no supporting data at the network level.

Includes walk and bike modes. Treats toll trips as a choice in the mode choice models.

No explicit treatment of low share modes. Includes walk and bike modes. Uses a measure of intensity of urban activity to explain non-motorized


No


Boarding fare is the average fare paid by all users, including pass holders. Toll cost is the average cost paid by all users.

No No


No No Subject to bias due to use of average zonal parking cost. No explicit treatment of transit or parking discounts.

Aggregation bias Subject to bias due to use of average zonal parking cost. No explicit treatment of parking discounts. Boarding fare considers average discount due to use of passes.


Real estate / Land prices, travel operating cost by income.


Travel cost by income for work trips. Travel cost for non-work trips.

None related to housing market. Existing mode choice model coefficients were not estimated with local data.



Only through sensitivity analyses Only through sensitivity analyses Only through sensitivity analyses



Yes, to the extent that they are accounted for in the Urban Development Model.

Yes



Yes. Demographic characteristics forecasted using cohort-survival techniques. Economic sector relationships forecasted using time series / regression models.

Yes, household totals by income and age are obtained from the land use model; travel model then applies worker and children sub-models.


No No; while cohort-survival is used in the demographic forecast, the travel demand model is not stratified by household characteristics.

No



Comparative Description of Selected Travel Demand Models (continued) Columbus San Francisco Portland (Tour) Treatment of Low Share Alternatives

Includes non-motorized modes as a mode choice. Home Interview Data supplemented with transit On-Board data to support mode choice estimation.

Includes non-motorized modes as a mode choice. Home Interview Data supplemented with transit On-Board data to support mode choice estimation. High level of zonal detail ensures adequate representation of transit walk accessibility. Includes several measures of urban form/economy to help explain propensity to walk.



Free parking entitlement explicitly accounted for.

Explicitly allocates workers on work trips to a free parking group or a full parking cost group. Uses income stratified cost coefficients.


No No

Aggregation bias No explicit treatment of transit or parking discounts, but accounts for free vs. pay parking choice.

No explicit treatment of transit or parking discounts, but accounts for free vs. pay parking choice.


Tour-based and trip based travel cost elasticities (toll, fare, parking, auto operating cost)

Tour travel cost by income. Trip cost.



Yes due to the stochastic nature of micro-simulation

Yes due to the stochastic nature of micro-simulation





Indirectly by controlling some household characteristics at the zonal level

Indirectly by controlling some household characteristics at the zonal level


No No



Comparative Description of Selected Travel Demand Models (continued) Denver Edmonton Oahu Ability to Address Policy Issues

Able to model TDM strategies and evaluate their effectiveness and impact on population subgroups

HOV strategies may be modeled in assignment only. Toll diversion is a function of toll cost only. Transit demand is not capacity constrained.

Toll and HOV strategies may be modeled in assignment only. Transit demand is not capacity constrained. Effects can be evaluated at person-type level.

Fully able to model land use policies and impact of congestion on land use decisions.

Predicts overall demand for PNR lots as well as daily turnover

Overall demand. Transit demand not capacity constrained.



Supports corridor / sub-area analysis

Yes Yes Yes

Explicit ‘behavioral’ modeling of freight movements – ability to address impact of freight on general traffic and vice-versa

No. Applies truck trip rates to total employment and households.

No. However, 2002 commodity flow survey is available.

No. Applies truck trip rates by employment type and distributes them with a choice model. Truck forecast is updated as the land use model employment forecasts are updated.

Predicts weekend travel

No No No

Provides reliable off peak travel times and non-commute travel estimates

Model calibrated to peak and off-peak observed speeds.

Unknown Unknown

Provides reliable estimates of suburban and rural traffic

Models facilities by area type to better address this.

Unknown Unknown

Provides reliable estimates of external traffic

External traffic growth through constant growth rates.

Unknown Unknown

Able to model “out of the ordinary” conditions: visitor travel, special events, seasonality, accidents, poor pavement conditions, repair and construction work.

Limited. No Includes a visitor sub-model. Able to consider development and land use events.

Treatment of air quality / noise implications

Improved by recent calibration to observed speeds.

No speed model, standard calculation of air quality impacts.

Addresses effects on water quality

No No No



Comparative Description of Selected Travel Demand Models (continued) Houston San Diego Portland (Trip) Ability to Address Policy Issues


Effect of Toll and HOV strategies can be evaluated at the mode choice level, and effects evaluated for household stratified by auto ownership.

May be able to model some land use policies. Able to consider HOV/non HOV tradeoffs and Toll/no Toll tradeoffs as well as competition among transit modes. Effects cannot be evaluated for household subgroups.

Able to model land use policies. Does not consider toll or HOV modes in mode choice.


Overall demand only. Transit demand is not capacity-constrained.

Overall demand only. Overall demand only.


Yes Yes Yes


No. Uses a fixed table.

Unknown No. Truck trips allocated to TAZs from regional estimates based on commodity flows.


No No No


Unknown Unknown Unknown






Includes corrections for seasonal trip-making in Galveston Island and Brazoria Beach.

The model estimation/calibration includes data from a visitor survey.

No


Applies a calibrated speed model to the link volume estimates.

Full model convergence is not assured.

Calibrated speed model applied post-assignment.


No No No



Comparative Description of Selected Travel Demand Models (continued) Columbus San Francisco Portland (Tour) Ability to Address Policy Issues


Strategies can be modeled through network effects and to the extent that they change the values of the various models’ explanatory variables. Effects can be aggregated for any household and individual subgroup.

Strategies can be modeled through network effects and to the extent that they change the values of the various models’ explanatory variables. Effects can be aggregated for any household and individual subgroup.


Yes Yes


Yes Yes


No. Includes an aggregate model based on trip rates, not iterated with the core model.

No, freight trips are obtained from MTC non-home based trip tables.


No No


Unknown Unknown


Unknown Unknown


Unknown Unknown


No No





No No



Comparative Description of Selected Travel Demand Models (continued) Denver Edmonton Oahu Application Program Characteristics

Data architecture and integration

Transportation conditions feed back to land use model.

No land use – network integration. Demographic & network inputs prepared at 5 year intervals. EMME2 platform.

Integrated land use & trip model demographic database. Consistent land use – trip model inputs. MINUTP platform, Java + MySQL land use application.

Size

~2,664 zones. 879 zones: 846 internal 33 external Highway, Transit, Walk and Bike networks 25 purposes

762 zones

Hardware requirements

Modest Unknown Substantial (UrbanSim).

Data requirements - from readily available data to extensive input forecasting requirements

Typical Mostly typical, requires forecasting total person trip market by TAZ.

Requires initial estimates of employment (16 categories), residential units, and land use type by grid cell (~5 acres each)

Ability to grow – encompass more or more detailed network and geography

Model script written to permit increase.

Unknown Unknown

Ease of update in light of new data, actual system performance


Modularity – not all decisions need to be resolved for all applications

Not modular Not modular Somewhat modular

Accuracy vs. Effort tradeoff

Relatively simple model, somewhat limited household and time of day segmentation, except in assignment stage. Mode choice model is somewhat too simplistic, particularly for non-work trips.

Overall good balance between a traditional four-step model and an activity model, but it lacks household stratification detail.



Comparative Description of Selected Travel Demand Models (continued) Houston San Diego Portland (Trip) Application Program Characteristics


Currently not integrated, but in the process of implementing UrbanSim. EMME2 platform, Java and Fortran application.

Comprehensive highway and transit network database, maintained in Arc Info. Integrated land use forecast and travel model inputs. TransCAD platform.

Integrated land use and trip model database. EMME2 platform, R application.

Size

2,600 zones, 9 trip purposes, work trips segmented by income and auto ownership, non-work segmented by auto ownership, 19 mode choices

Unknown ~1,300 zones.


Moderate ++ Modest Moderate


Typical Typical. Requires large detail to land use model to support household stratification. Requires two- digit SIC employment forecasting.


Application program designed for easy expansion.

Unknown Unknown


Easy. All models read text-based control files. Mode choice application includes an auto-calibrate option.

Unknown Unknown


Not modular Not modular, except at its two main stages.

Not modular, except perhaps the land use model.


Large effort required to iteratively calibrate the gravity and mode choice models, with no apparent gain over a simultaneous trip destination / mode choice model.

Quick execution and relatively simple models, but at the expense of richness in household/travel stratification.

Relatively quick execution and moderate number of models to maintain, yet sufficient stratification for most applications.



Comparative Description of Selected Travel Demand Models (continued) Columbus San Francisco Portland (Tour) Application Program Characteristics


No land use – network integration. TP+ platform, Java application.

No land use – network integration. Explicitly models travel by San Francisco residents only; non-resident travel taken from MTC model. TP+ platform, Java application

Size

1,800 zones, 1.4 million people. Relevant size measure is the product of zones and modeled population.

766 zones, 800,000 people. Relevant size measure is the product of zones and modeled population.


Substantial Substantial Substantial


Typical Typical


Unknown Unknown


Unknown


Modular Somewhat modular


Many models to estimate, but it provides a complete simulation of population activity and travel patterns.


SECTION 4

DESIRED MODEL FUNCTIONALITY AND

POTENTIAL STRUCTURAL ENHANCEMENTS


4. Desired Model Functionality and Potential Structural Enhancements

The IRM project team assembled a technical panel of regional planners and engineers and a policy panel of elected and appointed officials, which met twice during the Vision Phase of the project. The charge to these panels was to first assemble a list of desired attributes for the regional transportation model, based on their knowledge and experience in travel demand planning and policy in general and in using the DRCOG model for various transportation projects in particular. Subsequently, the panels were asked to distill the desired model attributes into a set of needs and to rank the top ten needs in order of priority. PB Consult together with a panel of modeling experts then assisted the project team in identifying potential model structural enhancements to address the policy and planning needs. The first part of this section discusses whether and how the various policy and planning issues raised by the planning and policy panels could be addressed through particular model structural features. It also discusses whether particular perceived shortcomings of the model are related more to data unavailability than to a lack of a structural feature per se. The section is organized around the top ten functional features identified by the planning and policy panels as needed model improvements, in decreasing order of importance, namely:

• Effects of development patterns on travel behavior • Sensitivity to price and behavioral changes • Effects of transportation system and system condition • Need for improved validity and reliability • Ability to evaluate policy initiatives • Better analysis of freight movement • Ability to show environmental effects • Modeling low-share alternatives • Better ability to evaluate effects on specific sub-groups • Reflect non-system policy changes (TDM, ITS)

Based on these needs, the specific characteristics of the existing DRCOG model and the previous discussion about best practice model characteristics, two sets of recommendations were made about potential improvements to the DRCOG travel demand model. The first set assumes that the overall model structural form remains trip-based. The second set assumes that tour-based models are adopted. The second part of this section discusses the proposed potential improvements and how they would address the needs listed above. In addition to discussing potential model structural features, this section also briefly touches upon two model requirements that relate more to the model’s application program: data maintenance and archiving and reporting capabilities.

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4.1. Desired Functional Attributes of the DRCOG Travel Demand Model

This section discusses whether and how the various policy and planning issues raised by the technical and policy panels can be addressed through particular model structural features. It also discusses whether particular perceived shortcomings of the model are related more to data unavailability than to a lack of a structural feature per se. The section is organized around the top ten functional features identified by the planning and policy panels as needed model improvements, in decreasing order of importance.

Effects of Development Patterns on Travel Behavior

This functionality requires the implementation of dynamic land use modeling as part of the travel demand forecasting process. Two of the inventoried models include dynamic land use modeling: Oahu, which uses UrbanSim, and Portland, which uses MetroScope. To deliver the functionality called for by DRCOG, the land use model needs to be able to model residential and employer location choice within a framework that includes measures of travel accessibility and recognizes that the land supply and demand equilibrium is a market-based process subject to regulatory directives. The land use model and the travel demand model need to be run consecutively and iteratively, each of them feeding information to the other at fixed-time intervals (typically five years) in order to arrive at a 20 or 30 year forecast. It is this manner of application, stepping through the land use and travel demand models iteratively, that allows development patterns to influence travel behavior, and travel behavior in turn to affect development patterns. Both UrbanSim and MetroScope use mode choice logsums, stratified by auto ownership to compute accessibility at the TAZ level. In addition, UrbanSim also uses other indicators of travel accessibility, such as distance to nearest highway, retail jobs within walking distance, etc. The land use models typically use a more disaggregate unit than the TAZ, but eventually aggregate their residential and employment forecasts at the TAZ level to feed back to the travel demand model. Due to the complexity of land use dynamics and the interplay with zoning and other regulatory ordinances, MetroScope provides an opportunity to subject the land use forecast to a policy review. This typically occurs after completion of the entire model-based forecast.

Sensitivity to Price and to Behavioral Changes

This functionality requires two complementary structural features. Since travel behavior is the result of individual decisions, then the basic unit of analysis in the model needs to be each person in the population. This lays the foundation upon which to build decision models for relevant travel-related choices. Such models would maintain household and tour/trip linkages and conditionality as necessary to properly represent choice sets and household dynamics, thus modeling the options, constraints and incentives that inform travel decisions. For the model to be sensitive to price, then price needs to be entered as an explicit variable in (some of) the decision models: tolls, fares and auto operating costs in mode choice, rent in land use models. Price (or more exactly cost) is typically a model input; however, in some cases, price may be a model output. For example, in a land use model rents are the result of market clearance dynamics and so are determined by the model. In a model where transit demand is sensitive to station parking capacity, shadow prices at the station level may be used as indicators of the parking fees that could be charged when demand exceeds capacity.

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Price sensitivity is also a function of income; this needs to be reflected in the choice of variables and segmentation for the mode choice model.

Impacts of Transportation System and System Condition

The panels called for the model to be able to quantify two types of effects: on development patterns, including location and price, and on travel behavior, including work choices and induced demand. The effects of system condition (interpreted here as levels of service on the highway network and transit system) on development patterns can be best examined by linking the transportation model with a dynamic land use model. As described above, a land use model run iteratively with the travel demand model would allow DRCOG to study both the effect of development patterns on system condition, and the effect of system condition on land use. DRCOG already has the ability to study some of the effects of system condition on travel behavior. This is accomplished by iterating the entire sequence of travel demand models until the speeds produced by the assignment model are consistent with the speeds assumed by the trip distribution and mode choice models. One additional step would be to ensure that such speeds adequately reflect existing levels of congestion. The panels called specifically for the ability to study the effect of system condition on work location choice and on induced demand. For the former, it would be necessary to implement a work location choice model. Although it would be possible to add such a model to the existing four-step model, it may result in a relatively contrived application, given the aggregate nature of the trip-based framework. A better way to implement this type of model would be to do it in a micro-simulation framework. Induced demand is a phenomenon that is still not well-understood, thus it is difficult to propose model improvements specifically geared towards quantifying it. To the extent that all model components, including trip generation, are responsive to system condition, it is expected that the model will reflect induced demand. In reality, DRCOG would be in a better position to quantify induced demand if it adopts a micro-simulation, tour-based framework. This framework allows estimating the number of trips per person as a function of system condition, substituting in-home activities with out-of-home activities, and it explicitly constrains the total daily activity schedule to be completed in a 24-hour period. All of these are key factors for studying induced demand that a trip-based, four-step model cannot consider.

Need for Improved Validity and Reliability

As identified by the policy and planning panels, the next-generation model should strive to improve forecasting accuracy for the following types of trips: non-work travel, internal-external trips, external-external trips, rural and suburban trip-making, commodity flows, non-motorized travel, and travel to/from special generators. Ways in which forecasting methods for each of these trip types are discussed next, with the exception of commodity flow models, which are addressed separately below. Regarding non-work travel, improved detail in the segmentation of the travel market (by trip purpose, for example), provides the basis for building destination and mode choice models that better capture the characteristics of these markets than aggregate home-based other models. Tour-based models would enhance the reliability of the non-work travel estimates by explicitly modeling the linkages and constraints that work imposes on these other types of trips. Tour-based models are not necessarily more demanding of the home interview survey than trip-based models. Internal-external and external-external trips are prone to larger estimation errors than resident trips because less is known about them. Ultimately IE and EE models rely on traffic counts and assumptions

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about traffic growth at external stations to produce reasonable forecasts. Rather than attempting to estimate models at the TAZ level (for IE trips), which typically have low explanatory power and whose reasonableness is difficult to assess, these models could be calibrated and forecasted at a more aggregate level (district or sub-region). A more novel approach involves forecasting economic activity for the entire state or a subset of the state (but larger than the region) in a land use model, so that the IE/EE model is not wholly dependent on the external station counts. To better predict rural and suburban trip making, sufficient zonal and network detail needs to be included in the model. Where the data show significant differences by area type in trip rates (not explained by differences in auto ownership, household size, etc.), consideration should be given to stratifying the trip rates by area type or other measures of urbanization, as well as formulating a strategy for forecasting these variables as conditions change over time. Sufficient attention needs to be given as well to forecasting population and employment growth, as well as to collecting sufficient traffic counts for use in model validation. The model needs to recognize that some locations present unique trip-generating situations, and should treat them accordingly. Locations that typically warrant use of sub-models include airports, colleges with large on-campus residences, places that attract large numbers of resident and/or visitor travel, large employment/residential centers such as military bases or other employment that generates large truck volumes, to name some. The choice of sub-model structure is a function of the data available and/or the cost of collecting data, and the specific impact that the location has on the local and regional travel forecasts. In order for the model to provide reliable estimates of trip-making measures across population subgroups, highway/street classes, modes, and geography, its structure needs to include sufficient detail in the representation of population subgroups, highway classification, modal availability, and geographic coverage and differentiation, and sufficient attention needs to be given to understanding and representing the differences among classes within each of these groups. And while structural features can be used to improve model reliability, by and large the model can only be as reliable as the data that are used to estimate it, calibrate it, and validate it. It should also be recognized that models cannot accurately predict traffic on every single facility or boardings on every transit route. They can however provide trends and indicate how population subgroups and facilities are impacted. For detailed planning or traffic studies, particularly those that focus on arterial corridors or sub-areas, careful consideration should be given to developing methodologies that further refine the model estimates based on detailed traffic counts or similar observations, or that use the model estimates as the input to more detailed traffic simulation studies.

Ability to Evaluate Policy Initiatives

This functional area is quite broad, and thus a detailed response cannot be provided at this point. In general, the model can reflect some policies with relative ease, for example HOV lane restrictions, free-fare districts, toll road charges, parking cost charges, among others. Other policies more geared towards highway and arterial operations, such as dynamic signal timing or the provision of traffic and transit information, are not easily reflected in a regional travel demand model. Some plausible effects could be incorporated in the model, for example an increase of average free-flow speeds to reflect better signal coordination, or a reduction in the maximum transit wait time to reflect better information about vehicle arrival times, but the model itself does not provide a direct ‘knob’ to turn. Moreover, in some cases the effect could only be incorporated in the model by assuming a transportation system response (for example, that dynamic signal coordination results in higher free-flow speeds), when in fact it is proof of this response what is being sought.

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Better Analysis of Freight Movements

Truck models can be improved when adequate land use and commodity flow data exists, both for internal flows and for internal to external flows. A typical application uses various techniques to produce a seed matrix, and then uses matrix estimation to perturb the seed matrix in a way such that the assignment estimation error (measured with respect to observed traffic counts) is reduced. The reliability of the model is a function of the quality and quantity of the traffic counts and the data used to produce the seed matrix, as well as the method and assumptions used to forecast the matrix. Another approach is to use trip rates that vary by industry type, and gravity or other models for trip distribution. Here too a rich dataset is required to estimate the trip rates, and some measure of economic activity by industry required as a model input for forecasting. In general the choice of model to use for commodity flows is largely independent of the passenger travel model structure.

Ability to Show Environmental Effects

There are two specific structural features that a travel model requires to be able to provide reliable information for environmental planning: a time of day choice model and accurate and reliable speed-delay functions or a post-assignment speed model. Additional desirable features include population micro-simulation at least through mode choice and GIS capabilities. Each of these is used in the calculation of air quality, water quality and noise impacts as follows: Air Quality

Air quality analysis relies on accurate estimates of facility volumes and average operating speeds for different times of the day. The time of day choice model provides this functionality. The Columbus model in particular models time of day at one-hour intervals, thus providing the travel demand input for 24 one-hour assignments. The number of assignments to be performed is limited due to the time needed to complete them, but this type of detail gives almost unlimited freedom in the choice of which periods (and length of time) to assign. Hand-in-hand with time of day volumes are the time of day speed estimates. For air quality purposes it is important to validate the speeds produced by the model for different times of day. If necessary, a post-assignment speed model may be required to improve the accuracy of the model speeds. In addition, air quality impacts sometimes include an estimate of the number of pollutants emitted during cold starts and hot soaks. Tour-based micro-simulation carried through mode choice would allow performing a time-in-motion analysis stratified by the duration of the previous activity, which would yield cold starts, or by the duration of the following activity, which would yield hot soaks. Noise Impacts

Noise impacts are a function of road volumes, and particularly a function of the maximum volume that a road carries before the speed starts to degrade. Often, this occurs during the off-peak periods. Thus as is the case for air quality, for noise impact studies the time of day model is the key structural feature of the travel demand model. An additional piece of information for noise impact study is the ability to measure the number of residential units that are located within a given distance of a corridor. This functionality can be easily achieved with geographic analysis when the model uses an integrated land use and highway network GIS-based database. Water Quality

Water quality impacts of road projects are typically calculated on an annual basis as a function of average day volumes. The ability to produce volumes by time of day may be of less use for this type of study, since it would require very detailed interaction with the hydrological characteristics of the region. However, one way to potentially improve the information for water quality would be do introduce seasonality in the model forecast, whether by specifically modeling seasonal events or travel, or by

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providing confidence bands for the volume estimates. Tour-based micro-simulation gives an indication of volume variability because it is a stochastic process. It would be necessary to study however whether the inherent variability in the model forecasts is sufficient to account for seasonality. Alternative techniques could involve pivoting off seasonal traffic curves from point volume estimates.

Modeling Low-Share Alternatives

Many mode choice models include low share and/or non-motorized modes in the choice set. However the ability to include these modes in estimation and calibration, as well as the accuracy of the forecasts largely depends on the quality and quantity of available mode-specific data. For transit modes, these data are obtained from on-board surveys, which in turn need to be designed to capture an adequate number of observations for all modes and access options. For toll and HOV modes, surveys specifically targeted to toll and HOV users need to be conducted, since the home interview survey is unlikely to capture a large enough sample of these users. For non-motorized modes, the challenge is two-fold: having a rich enough sample of non-motorized trips, and being able to properly represent the physical environment in which these trips tend to be made. For the latter, the approaches used in Columbus to represent transit walk accessibility, and in Edmonton and San Francisco to measure ‘pedestrian friendliness’ should be given further consideration. The level of detail devoted to the forecasting of these trips should be commensurate with the amount of information required for planning purposes, yet consistent with the ‘scale’ of a regional travel demand model. For example, while the model may not be able to accurately predict pedestrian and bicycle flows, it should respond to policies that encourage non-motorized travel, both locally and regionally.

Better Ability to Evaluate Effects on Specific Sub-Groups

This functionality calls specifically for two of the structural features described in Section 3, namely the ability to model travel decisions in a disaggregate fashion and explicitly considering individual and household characteristics in the model form. In addition, it calls for enhanced reporting features in the application program. Both of these structural features are best accomplished with the use of tour-based micro-simulation models. The output of a micro-simulation model is a full itinerary of trips and activities for each person in the population, including departure times, activity purposes and location, intermediate stop purposes and locations, modes used and party composition. Given this output, tabulating various system performance measures across household and individual characteristics is a simple matter. It should be recognized, however, that meaningful tabulations can be accomplished only for the attributes explicitly used as explanatory variables in the model. For example, while the population synthesizer may forecast age for all population individuals, if none of the models use age as a variable or market segment, any differences observed in system performance by age are likely to be spurious or due to non-accounted for factors. Hence, careful thought needs to be given to the population groups upon which it is desired to measure system performance. Secondly, it should also be recognized that system performance measures that come from the highway and transit assignment process cannot be linked to household and individual characteristics. The state-of-the-art for regional travel demand models is still to use zone-based assignment techniques. While it’s possible to keep track of the trips on various trip tables, most commercially available packages have practical limitations on the number of tables that can be used for a multi-class assignment. It is possible though to link assignment performance measures to person characteristics in a traffic simulation model. Currently these models operate at the corridor level; region-wide traffic simulation is still a research subject.

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Reflect Non-System Policy Changes (TDM, ITS)

This is another broad need area that requires better definition before specific modeling improvements can be proposed. In general, regional travel demand models do not lend themselves easily to the evaluation of TDM and ITS policies. However, some policies can be adequately modeled and their effect on travel behavior and system condition examined. Depending on the policies that need to be examined, DRCOG may want to complement the travel demand model with other analytical tools, such as network simulation models.

Improved Data Interchange, Maintenance and Archiving

This functionality could be achieved by using database tools, and possibly a GIS-based database. Of course, whether it is truly functional and efficient depends on the actual design of the database and its user interface. The database should strive to integrate the land use and network data, and could even be designed to integrate survey data and model outputs (whether micro-simulation outputs or trip tables). A set of utilities could be designed to perform scenario management functions, including building scenario-specific networks from a base network and a list of projects, and archiving model output.

Improved Reporting Capabilities

This functionality should be directly addressed by the design of the model application program. The application program is comprised of the travel demand software (in this case TransCAD) and other software specifically written for DRCOG. Some reports can be automatically produced as part of a model run. Other standard reports may be available in a library of reporting utilities, using control files to customize the report as needed (for example, to choose the origin and destination for a travel time report). Some of these reports can exploit the capabilities of TransCAD, while others may be more efficiently implemented in the DRCOG-specific software.

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4.2. Potential Structural Improvements to the DRCOG Travel Demand Model

Based on the previous discussion about best practice model characteristics, the travel demand model functionality desired by DRCOG and its partner agencies and the specific characteristics of the existing DRCOG model, two sets of recommendations were made about potential improvements to the DRCOG travel demand model. The first set assumes that the overall model structural form remains trip-based. The second set assumes that tour-based models are adopted. Note that many of the recommendations in the first set apply to the tour-based models as well. These recommendations are further elaborated into more specific model forms in the next section of this document.

Trip-Based Model Improvements

One strategy for building the next-generation DRCOG model is to build upon the existing trip-based model, adding structural features that would it give the functionality desired by its customers. Implementation of this first set of recommendations would satisfy the modeling needs defined by the policy and planning panels that can be cost-effectively addressed in the trip-based framework.

• Model travel decisions in a more disaggregate way, either by segmenting the travel market further (more trip purposes, use of time of day segmentation and auto ownership and/or other household-based segmentation).

• Implement a dynamic land use model. • Include additional consideration of household characteristics in all model steps, including auto

ownership and workers sub-models, further segmentation of trip production rates, further segmentation of the trip distribution models (time of day and household-based) and further segmentation of the mode choice model (time of day, auto ownership or car sufficiency). Include household and person specific variables in mode choice, such as income-stratified cost coefficients.

• Estimate, calibrate and validate destination choice models to substitute for the gravity models, using mode choice logsum and other accessibility measures. Estimate and calibrate separate models for work trips and for different types of non-work trips. Segment the models by auto ownership, ensuring separate consideration of the zero-car household models.

• Estimate, calibrate and validate a more comprehensive set of mode choice models, including further purpose segmentation, consideration of time of day differences within each purpose, a larger choice set, in particular including non-motorized modes, HOV and Toll options, and separate consideration of transit modes. Include household characteristics. Include indicators of pedestrian friendliness and other non-motorized level of service variables. Apply transit walk-accessibility segmentation. Develop mode-specific constants in a manner consistent with FTA directives.

• Implement capacity-restraint transit assignment. • Validate the speeds obtained from the volume-delay functions by time of day (at least four

periods), or calibrate a post-assignment speed model. • Validate traffic volumes for at least two and possibly four time periods, in addition to 24-hour

volumes. • Implement a commodity flow model, if possible by linking it to the land use model and including

geography from outside the region. • Implement IE and EE models, if possible by linking them to the land use model and including

geography from outside the region. • Develop a GIS-based database for network and zonal data (including trip tables). • Develop extensive reporting capabilities.

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Tour-Based Model Improvements

The advantages of implementing tour-based models are numerous. The models are more rigorous in that they are based on utility maximizing decision theory. They are estimated and applied in a disaggregate fashion. Their stochastic nature allows developing bounds for the model estimates. They allow implementing all of the improvements described above for trip-based models, and they allow three additional enhancements:

• Model explicitly intra-household interactions and joint travel; • Maintain linkages throughout the entire decision chain, thus allowing lower level decisions to be

consistent with upper level decisions; • Implement time of day choice models with a high (one hour) resolution.

The tour-based models would also potentially provide more detailed analysis of system performance impacts on the population than trip-based models, without sacrificing the detail that is already available for sub-areas and highway facilities. Many of the recommendations for structural improvements in the trip-based framework are also recommended for implementation in the tour-based framework, thus they are not repeated here. In fact, because the tour-based framework is able to support all the modeling needs identified for Denver, and also because it has more potential as a platform for future model development than the trip-based framework, the project team tasked PB Consult with developing design alternatives for a DRCOG tour-based model. The next section contains a detailed description of the model structural improvement recommendations to be applied should a tour-based framework be adopted for DRCOG.

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SECTION 5

DESIGN ALTERNATIVES FOR A TOUR-

BASED MICRO-SIMULATION TRAVEL DEMAND MODELING SYSTEM


5. Design Alternatives for a Tour-Based Micro-Simulation Travel Demand Modeling System

5.1. Introduction This section describes alternative designs for the new travel-demand modeling system proposed for the major update of the DRCOG regional model. It uses the conventional four-step modeling approach as a reference point and introduces the proposed modeling enhancements grouped in four consecutive stages of development. The minimal time and budget framework for the project corresponds to the first stage. Stages 2 to 4 can be considered as project extensions if some of the proposed modeling enhancements are recognized as important in view of the policy and planning needs of agencies using the DRCOG model. All the proposed modeling components described in the memo (including some of the advanced features of stages 3 and 4) can be practically implemented within a reasonable time and budget framework and without excessive data requirements or risk of failure. These components have been carefully chosen from the already implemented exhaustive research and successful practical experience in the United States and worldwide. However, the additional complexity of each successive stage will require additional time and budget for technical steps for estimation, calibration, programming implementation, and application of the model. Each successive stage of development includes all the enhancements made at the previous stage. Many of the modeling components and data preparations are the same through all or some of the stages. Thus, development of each successive stage in addition to the previous one will require only marginal time and budget additions for the majority of the modeling components. Most of the modeling components are designed in a modular way allowing replacement of them one by one, gradually moving from stage to stage rather than replacing the whole modeling system at once. Thus, at any stage of model development (after stage 1 has been implemented) DRCOG will have a working modeling system with some enhancements on the way but without interruption of the practical application process.

Basic Features of the New Generation of Travel Demand Models

The new generation of regional travel demand models in the United States currently includes the San-Francisco County Transportation Authority (SFCTA) model, Portland METRO model, New York Metropolitan Transportation Council (NYMTC) model, Mid-Ohio Regional Planning Commission (MORPC) model and Atlanta Regional Commission (ARC). Comparing to the conventional 4-step model (referred to as “stage 0”) the new generation of models is characterized by the following three positive features fully preserved in the proposed modeling system (stages 1 to 4):

• Tour-based structure where the tour that is a closed chain of trips starting and ending at the base location (home or workplace) is used as the base unit of modeling travel instead of the elemental trip; this structure preserves a consistency across trips included into the same tour, by such travel dimensions as destination, mode, and time of day (TOD). In particular, the whole spectrum of travel dimensions (mode, destination and TOD) related to non-home-based trips can be properly linked to the relevant home-based trips.

• Activity-based platform, that implies that modeled travel be derived within a general framework of the daily activities undertaken by households and persons including in-home activities, intra-

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household interactions, time allocation to activities, and many other aspects pertinent to activity analyses, but typically missing in the conventional travel demand models.

• Micro-simulation modeling techniques that are applied at the fully-disaggregate level of persons and households, which convert activity and travel related choices from fractional-probability model outcomes into a series of “crisp” decisions among the discrete choices; this method of model implementation results in realistic model outcomes, with output files that look very much like a real travel/activity survey data. Explicitly modeling a full list of households rather than zonal strata of households with identical attributes, avoids numerous aggregation biases arises in the conventional modeling framework. It allows for more realistic and consistent linkage across travel choices made by the individual in a course of a day. Micro-simulation technique greatly reduces a size of the model intermediate outcomes to store and handle and, as a result, opens a way to apply a much higher level of segmentation (number of trip purposes, modes, household and person attributes) as well as of a spatial resolution (number of transport analysis zones and access sub-zones).

These three features constitute a fundamental core of the approach and were already incorporated in the first new-generation models developed for the Portland METRO [Bowman et al, 1998; Bradley et al, 1999], SFCTA [Bradley et al, 2001; Jonnalagadda et al, 2001], and NYMTC [Vovsha et al, 2002]. Explicitly modeling a full list of individual households and persons in the region at the level of travel details close to the reality does not mean that the model system design is intended to pinpoint each individual behavior in each target year. In line with the basic paradigm of the disaggregate demand models this level of details is only a way to better predict aggregate travel statistics that are the focus of interest of transport planners. However, a better aggregate prediction of transportation flows is ensured by means of a more realistic and consistent representation of the underlying individual travel choices. Though at the individual level even a perfect behavioral simulation cannot predict travel choices exactly because of the inherent random variability of individuals, multiple individual simulations are summed to a reasonable prediction of the aggregate travel statistics of interest. The same way, though the probability theory is helpless to predict a particular outcome of one toss of a coin, it gives almost exact prediction of the aggregate outcome of one thousand tosses. The right unbiased core probability model (50/50 in the coin tossing case) serves as a useful tool for aggregate predictions, not for individual outcomes. However, a quality of aggregate predictions is a direct function of the statistical quality of the individual core model. Thus, application of the individual simulations at the detailed level helps properly capture numerous internal sensitivities of various population groups to changing travel conditions or land-use developments that would be otherwise inevitably lost in aggregation biases. The project framework does not include development of a conventional 4-step model that is referred to as stage 0. However, in view of the complexity of micro-simulation and tour-based modeling framework it can be useful to develop and calibrate a stage 0 conventional model in parallel with a stage 1 development. Both models will share many features including travel segmentation, date preparation and estimation procedures whenever it is possible to reconcile trip-based with tour-based and fractional-probability with micro-simulation approaches. In particular, a micro-simulation model at stage 1 can have a background feature allowing for an explicit storage of accumulated fractional-probability matrices of tours and trips along with the discrete simulated choices. This will allow for comparison of the results, comprehensive model validation, as well as investigation of the Monte-Carlo error and variability of the micro-simulation procedure. It will require a 20% addition to the budget agreed for stage 1. For more advanced modeling structures (stages 2-4) that essentially exploit specific structural advantages of micro-simulation it is impossible to track back relation to the 4-step model and only the final network assignment results can be compared.

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It should be noted that comparison and validation of trip matrices (by purpose, mode and period of a day) is still possible across all stages 0-4, however, tours from stages 1-4 for this purpose will be broken into elemental trips including non-home-based components. However, it is impossible to re-construct tours from trip-based predictions made by the model system of stage 0.

Advanced Features Added Recently

Several new features and enhancements were incorporated in the recently completed Columbus (MORPC) model as well as in the Atlanta (ARC) model currently being developed. They reflect the growing body of research on activity-based modeling and micro-simulation for the last years. Two important and inter-related aspects have been frequently in the focus of research – intra-household interactions and time-use framework that proved to be of critical importance for describing and modeling individual activity and travel behavior. In particular, works of Arentze & Timmermans, 2000; Borgers et al, 2002; Ettema et al, 2004; Fujii et al, 1999; Gliebe & Koppelman, 2002; Golob & MacNally, 1997; Meka at al, 2002; Simma & Axhausen, 2001; Scott & Kanaroglou, 2002; Srinivasan & Bhat, 2004; Zhang et al, 2002; Zhang et al, 2004; and Zhang & Fujiwara, 2004 give examples of models for time allocation and activity episode generation between various type of activities and household members that provide valuable insights into the intra-household decision-making mechanism. Comparing to the previous model design, the new structures of MORPC and ARC represent two significant steps further in a better and more realistic description of travel behavior along these two lines:

• Explicit modeling of intra-household interactions and joint travel that is of crucial importance for realistic modeling of the individual decisions made in the household framework and in particular for choice of the high occupancy vehicle (HOV) as travel mode. The original concept of a “full individual daily pattern” that constituted a core of the previously proposed activity-based model systems [Bowman & Ben-Akiva, 1999; Bowman & Ben-Akiva, 2001; Bhat & Singh, 2000] has been extended in the MORPC and ARC systems to incorporate various intra-household impacts of different household members on each other, joint participation in activities and travel, and intra-household allocation mechanisms for maintenance activities [Vovsha et al, 2003b, 2004a, 2004b].

• Enhanced temporal resolution (of 1 hour or less) with explicit tracking of available time windows for generation and scheduling of tours instead of the 4-5 broad time-of-day periods applied in most of the conventional and also activity-based models previously developed. The time-of-day choice model adopted for MORPC and ARC with further enhancements is essentially a continuous duration model [Vovsha & Bradley, 2004] transformed into a discrete choice form. The enhanced temporal resolution opens a way to explicitly control the person time windows left after scheduling of each tour and use the residual time window as an important explanatory variable for generation and scheduling of the subsequent tours.

The proposed enhancements are not just technical. They represent reflections on the natural and logical “evolution” of the model system structures in certain conceptual directions some of which are already quite formed into operational structures while some other ones will be explored in future.

Organization of the Section

This section is organized in the following way. The next three parts correspond to a detailed analysis of the new conceptual components and modeling structures emerged. They are organized by the three next-generation model main features mentioned above, namely tour-based modeling techniques, a discussion of various aspects of the activity-based platform, and micro-simulation issues.

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The fifth part combines the previously discussed modeling components and approaches in the regional model design framework. It contains typical model system design and preliminary specification of the main models by the stages 1-4 of the model system development. The 6th part gives an overview of the model system enhancements and data requirements from stage to stage. This document is intended for travel demand modelers who are familiar with the conventional four step modeling technique but may not be familiar with tour-based, activity-based, or micro-simulation techniques. Explanation of the new model components in the first three parts is essential for understanding the model system design and staging subsequently proposed. Readers who are already familiar with the tour-based modeling basics and literature on activity-based modeling and micro-simulation can go to the model system design part directly.

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5.2. Tour-Base Modeling Technique and Terminology Though the final travel-demand dimensions for each elementary trip (purpose, origin, destination, mode, time of day, network route) are very much the same as in the conventional four-step approach, there is a fundamental difference in the way they are combined and sequenced in the tour-based modeling procedure. The main conceptual components of the tour-based modeling approach along with the related terminology are described below. These components are explained in relation to the corresponding conventional terminology of the four-step travel demand model.

Trip, Journey, and Tour

The conventional demand modeling technique uses trip as a unit of travel. Trip is defined as any movement between two locations: origin and destination. The fact that elementary trips of the same person during the day are linked has been recognized in the conventional modeling framework since long ago and some modeling enhancements have been tried to account for trip chains. However, the definition of trip chain proved to be too loose and the aggregate zonal approach did not really allow for development of operational models for trip chains. A step further towards operational models has been done with a definition of tour as a special trip chain that is “closed” in a sense that the origin of the first trip is the same as the destination of the last trip. Thus, formally tour has only one spatial anchor – origin that can be combined with several successive destinations. In vast majority of cases tour origin is associated with either residential or working place. Requirement from the tour to be a closed chain leads to extremely important properties that make this unit of travel operational in the modeling procedure. First, the mode-choice decisions are in many respects entire-tour-based rather than elementary-trip-based though there is a certain degree of freedom at the trip level as well. In particular, upper-level mode choice between private and public mode is almost exclusively entire-tour-based because real availability of private car exists only at the home end of each tour. Second, the number and destination location of the non-home-based trips is a function of the corresponding (preceding and following) home-base trips. Third, departure time for each successive trip in the tour is limited by the arrival time for the previous trip. However, the formal concept of a multi-destination tour proved to be too complicated for direct modeling. The further step in making operational framework has been made by introducing journey or half-tour based on distinguishing one particular destination as primary and treating all other destinations as secondary stops on the way to and from the primary destination. This definition is especially appealing for commuting to mandatory activity locations (work, school, university). It is less unambiguous for non-mandatory activities (household maintenance and discretionary) where some tours look as really multi-destination. However, in most practical cases the distinction between primary and secondary destinations can be reasonably made based on the activity type, duration and also distance from the tour origin. If several destinations have approximately the same activity type and duration, the farthest from the origin is considered as the primary destination of the tour. The journey from the tour origin to the primary destination is often referred to as outbound or direct while the journey from the primary destination back to origin is referred as inbound or return. Table 5.2.1 below shows a practical example of the primary destination specification rules adopted for the ARC model. Each destination on the tour has been assigned a priority index based on the activity type and total duration of the activity and travel time from home to the location and from the location back home. The lower index corresponds to the higher priority. The destination with the highest priority (lowest index) across all destination visited on the tour is considered as the primary destination. Activity at the primary destination defines the travel purpose of the entire tour.

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Table 5.2.1. Example of activity priority setting for the primary tour destination definition (ARC) Original survey activity code Model purpose Priority index of activity by total duration and travel

time from and to home, min No Activity No Purpose 0 60 120 180 240 300 360 420 480

1 Eating, preparing meals 7 Eating 12.0 7.0 5.0 4.5 4.4 4.3 4.2 4.1 4.0 2 Entertainment 9 Discr. 14.0 8.0 6.0 5.5 5.4 5.3 5.2 5.1 5.0 3 Visit friends, relatives 8 Visit 14.0 8.0 6.0 5.5 5.4 5.3 5.2 5.1 5.0 4 Working 1 Work 8.0 4.0 2.0 1.5 1.4 1.3 1.2 1.1 1.0 5 Work related business 1 Work 8.0 4.0 2.0 1.5 1.4 1.3 1.2 1.1 1.0 6 School / University 2/3 Sch/Un 8.0 4.0 2.0 1.5 1.4 1.3 1.2 1.1 1.0 7 Incidental shopping 5 Shop 12.0 7.0 5.0 4.5 4.4 4.3 4.2 4.1 4.0 8 Major shopping 5 Shop 10.0 6.0 4.0 3.5 3.4 3.3 3.2 3.1 3.0 9 Watching children 6 Maint. 10.0 6.0 4.0 3.5 3.4 3.3 3.2 3.1 3.0

10 Household work 6 Maint. 12.0 7.0 5.0 4.5 4.4 4.3 4.2 4.1 4.0 11 Fitness Exercising 9 Discr. 14.0 8.0 6.0 5.5 5.4 5.3 5.2 5.1 5.0 12 Outdoor recreation 9 Discr. 14.0 8.0 6.0 5.5 5.4 5.3 5.2 5.1 5.0 13 Medical, dental 6 Maint. 10.0 6.0 4.0 3.5 3.4 3.3 3.2 3.1 3.0 14 Community, political, civic 9 Discr. 10.0 6.0 4.0 3.5 3.4 3.3 3.2 3.1 3.0 15 Worship, religious 9 Discr. 10.0 6.0 4.0 3.5 3.4 3.3 3.2 3.1 3.0 16 ATM, banking, post office 6 Maint. 12.0 7.0 5.0 4.5 4.4 4.3 4.2 4.1 4.0 17 Waiting for transportation 0 Not modeled as activity destination 18 Drop off, pick –up someone 4 Escort 8.0 6.0 4.0 3.5 3.4 3.3 3.2 3.1 3.0 19 Sleep 9 Discr. 18.0 10.0 7.0 6.5 6.4 6.3 6.2 6.1 6.0 21 Rest, relax 9 Discr. 18.0 10.0 7.0 6.5 6.4 6.3 6.2 6.1 6.0

22 Drop off, pick up something 6 Maint. 12.0 7.0 5.0 4.5 4.4 4.3 4.2 4.1 4.0

23 Personal (bath, shower) 9 Discr. 18.0 10.0 7.0 6.5 6.4 6.3 6.2 6.1 6.0 24 Personal Business 6 Maint. 12.0 7.0 5.0 4.5 4.4 4.3 4.2 4.1 4.0 25 Volunteer work 9 Discr. 10.0 6.0 4.0 3.5 3.4 3.3 3.2 3.1 3.0 26 Getting Ready 9 Discr. 18.0 10.0 7.0 6.5 6.4 6.3 6.2 6.1 6.0 27 Other at home activities 9 Discr. 18.0 10.0 7.0 6.5 6.4 6.3 6.2 6.1 6.0 28 Work from home 1 Work 8.0 4.0 2.0 1.5 1.4 1.3 1.2 1.1 1.0 97 Other 9 Discr. 20.0 10.0 7.0 6.5 6.4 6.3 6.2 6.1 6.0

The original activity codes of the household survey 1 to 28 and 97 were aggregated into 9 travel purposes (1-work, 2-school, 3-university, 4-escorting, 5-shopping, 6-other maintenance, 7-eating, 8-visiting, 9-discretionary). Each activity was assigned a series of priority indices by duration with a 60-min step. In the survey data processing these indices were used as pivot points while the priority of each reported activity was assigned based on the actual duration by linear interpolation/extrapolation between/beyond the pivot points. Total duration included duration of the activity itself as well as associated travel time (off-peak highway time was used for simplicity) to and from the location. Travel time was considered separately for each location relative to the home location for home-based tours. For non-home-based sub-tours (at work, at university, at school) the corresponding workplace / school location served as the spatial anchor instead of home. The adopted structure of priority indices assumes some overlap between activity types. For example, mandatory activities (work, school, university) with a normal duration of 3 hours or longer always take precedence over non-mandatory activities. However very short mandatory activities that last 2 hours or less (that is just dropping in the workplace or school), are considered of lower priority compared to substantially longer non-mandatory activities.

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Numerous statistical trials were implemented with different index structures. The adopted structure is characterized by the best match between the activity of the chosen primary destination and the entire-tour purpose defined by the maximum cumulative time spent on each activity type across all destinations (97%). Thus, for 97% of tours, the primary destination defined by these priorities corresponds to the activity with the highest cumulative duration across all destinations of the tour. Only for 3% of tours activity at the primary destination did not correspond to the longest cumulative activity on the tour. This most frequently occurred to escorting tours with some additional maintenance stops on the way since escorting activity is considered of comparatively high priority (because of time constraints and inter-person interactions involved) but it frequently has a short duration; thus additional stops on these tour frequently accumulate more duration compared to the escorting itself. The hierarchy of travel units “tour-journey-trip” proved to be extremely effective and operational in demand modeling. In particular the following advantages should be mentioned:

• A number of tours made by a person in the course of a day is significantly limited (more than three is a rare case) comparing to a number of elementary trips that can be easily numbered over ten. This has created necessary premises for classification of typical patterns and modeling of the entire daily activity pattern rather than sets of independent trip productions by purpose made by each person. The “forest” of the travel behavior (typical pattern configurations) stemming from the daily activity agenda has become seen rather than separate “trees” in a form of numerous trips made.

• Each journey constitutes essentially a trip to the primary destination if we “skip” temporary intermediate stops. Thus, in many respects journey-based models are similar to the trip-based models. Both journeys and trips share the same modeling dimensions – purpose, destination, mode, departure & arrival time with the exception of stop frequency and location attributable to journeys only. However, within the trip-based concept there is no distinction between primary destination, say, workplace where person spends nine hours and commuting can take two-three hours more and a secondary stop on the way to work to drop-off a passenger that virtually takes few additional minutes for both travel and activity participation. Trip-based concept that obscures this distinction produces a significant percentage of non-home-based trips (normally 30% of the daily travel) that are difficult to model reasonably because of the very loose relation of their origins and destinations to the land-use anchors, not speaking about mode choice that is usually wrongly attributed to the network level of service as if it were made independently from the other trips in the tour. A real percentage of essentially non-home based tours (at work, at school, at university) is about 6-7% of the daily travel. In a similar way, an average daily percentage of home-based-work trips in conventional trip based models frequently falls below 15% while percentage of work tours is normally about 30%.

• Tour-based modeling technique ensures a full consistency of mode, destination and time-of-day choices for all trips from the same tour. In combination with the individual micro-simulation it also ensures a full consistency between home-based and related non-home-based tours of the same person. For example time and mode choice for at-work tour is conditional on the time and mode chosen for the base tour to work. This avoids necessity of synthetic modeling non-home-based trips as an additional trip purpose.

• In many cases for non-mandatory activities, tour proved to be a better and more reliable unit for modeling activity itself compared to individual episodes associated with each trip. The surveyed person has a better chance to memorize and report properly starting and ending times of say, shopping tour in a form (“I left home at 5:30PM for shopping and was back home at 7:00”) than a sequence of all visited locations with exact timing and address. Significant underreporting of short stops is a known and unavoidable drawback of household surveys that create “fuzzyness”

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in the statistical analysis and modeling based on a number of activity episodes and associated trips.

The following example illustrates the advantages of the proposed methodology – see Figure 5.2.1. Consider three persons with different daily activity agendas and individual tours. The first person implements work tour and independent shopping tour without stops. The second person implements work tour with at-work sub-tour for lunch and a stop for dropping-off a passenger on the way to work and another stop for shopping on the way home. The third person implements a maintenance tour with two stops for shopping in addition to the primary shopping destination and then an additional tour for dining out. Primary destinations are distinguished by a bold type while secondary stops by italics.

Home Work

Shop

1st person

2nd person

Home Work

Shop

Drop-off

Eat out

3rd person

Home Shop

Shop

Shop

Eat out

Figure 5.2.1. Various Configurations of Daily Travel

Table 5.2.2 below translates this figure into first, a tour-based and then, trip-based terminology. It is assumed that activities like shopping, escorting passengers and eating out are all classified as maintenance. Also in line with the most conventional models non-home-based trips are defined as a single class (additional purpose). Though in both terminological schemes there is exactly the same underlying travel and activity agenda, the operational description looks different and this can lead to quite different consequences for modeling. It can be seen that from the tour perspective first two persons have similar daily patterns built around their commuting to work that can be easily switched under certain circumstances. The underlying trade-off between implementing an additional maintenance tour from home versus making a stop on the way home from work is one of the most flexible components in the travel activity schedule. Additional drop-off and at-work sub-tour are generally considered as secondary details comparing to the base work tour. Contrary to that the third person who is quite probably a non-working adult is involved in maintenance activity only and has a distinctive activity pattern.

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Table 5.2.2. Comparison of the Tour and Trip Terminology

Persons Tours Journeys Trips

Home-Work Home-Work Work

Work-Home Work-Home

Home-Maintenance Home-Maintenance

1st person

Maintenance

Maintenance-Home Maintenance-Home

Home-Maintenance Home-Work

Non-Home-Based

Non-Home-Based

Work

Work-Home

Maintenance-Home

Work-Maintenance Non-Home-Based

2nd person

At work

Maintenance-Work Non-Home-Based


Non-Home-Based Non-Home-Based

Maintenance

Maintenance-Home Maintenance-Home


3rd person

Maintenance Maintenance-Home Maintenance-Home

However, comparing the same travel patterns under the trip terminology is completely misleading. Trip sets of the second and third persons look almost identical with a mix of multiple maintenance and non-home-based trips. Consideration of tour as the unit of travel required revision of the modeling approaches to such important travel dimensions as mode, destination, and TOD. According to the tour-based modeling paradigm, these dimensions should be consistently modeled for all trips of the tour. Thus, conventional 4-step modeling technique that considers each trip independently should be replaced with a more advance approach that preserves a unifying entire-tour dimension. The next two sub-sections outline approaches developed for modeling mode and destination choices. Discussion of the TOD choice model is postponed to the next section on activity-based platform since it is closely intertwined with general issues of time use and allocation to activities.

Bi-Level Mode Choice

This modeling component has been successfully applied in the SFCTA and Portland METRO models [Jonnalagadda et al, 2001] as well as (with some simplifications) in the NYMTC and MORPC models. The rational behind this approach is that mode choice decision is essentially an entire-tour decision especially when it is considered at the principal-mode level (auto versus transit). However, details of particular transit and auto modes can be better attributed to the trip level because they are dependent on the exact location of origin and destination of the particular trip. Thus, traveler can change modes during the trip chain, however, within the framework of the chosen entire-tour mode combination. This gives rise to the bi-level modeling structure where entire-tour mode is modeled at the upper level while the detailed trip mode is modeled for each trip conditional on the chosen entire-tour mode. This structure ensures a full consistency of mode choices across all trips within the tour that cannot be achieved in the conventional trip-based framework.

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It proved also to be beneficial to model exact frequency and location of intermediate stops on each journey between the two mode-choice stages because mode is an important determinant for the stop frequency and location. The following additional details that have not been considered in the SFCTA and Portland METRO models but are proposed for the DRCOG modeling system can be mentioned:

• The trip-level mode choice is not only conditional on the main tour mode, but also takes into account the previously made trip-mode decisions according to the predetermined order of trips (usually, by natural sequence of implementation) within the tour. In particular for tours without secondary intermediate stops the same mode should be mostly modeled for both half-tour directions.

• Park & ride mode includes an interchange (parking lot) location as an additional mandatory stop (it can be either a separate choice model or extraction form the network assignment procedure or even a part of a network coding depending on the way how P&R is modeled in the network) and it is required to use auto mode on the last trip back from the interchange to the tour origin. Correspondingly, there should be either a simple separate model or a sub-level in the joint mode & stop frequency model that orders other stops versus the mandatory stop at the interchange (for example if there is one outbound stop it is important to know whether it occurs before or after the interchange).

• When considering trip-level mode choices for transit the fare policy is taken into account. In particular, transit fare for each subsequent mode within the tour is calculated based on the policies applied in the region. It is known that over 90% of transit passengers use the same mode and line for the return half-tour as for the outbound half-tour. In terms of “complexity vs. reasonability” it is probably a reasonable assumption that mode combination should be replicated (in reversed order) for the return trip rather than modeled. In the NYMTC model we assumed that mode choice is the same for both half-tours. It should be noted that, in the micro-simulation framework, there can be many illogical mode flips even if the core probability is the same for both half-tours.

• In view of the explicit modeling of joint household tours the HOV occupancy (known after stage of daily activity-travel patterns for all joint tours) can be preserved for all trips within the tour for stages 1-3 of the model development. Of course, in reality there can be changes in the occupancy during the tour (pick-ups and drop-offs) but it is proposed to postpone partially joint travel to the advanced stage 4.

The following main entire-tour modes will be considered:

• “Park and Ride” • “Kiss and Ride”, • Transit with walk access and egress, • Drive alone, • Shared ride – driver and 1 passenger, • Shared ride – driver and 2 or more passengers, • School bus (with any additional access and egress mode combinations), relevant only for school

tours and is not modeled further at the stop-location and trip-mode stage, • Non-motorized (if not skimmed off by the pre-mode choice model), is not modeled further at the

stop-location and trip-mode stage.



The following trip modes will be considered:

• Drive alone, • Shared ride – driver and 1 passenger, • Shared ride – driver and 2 or more passengers, • Bus with walk access, • Rail (or any other premium mode) with bus and walk access,

In general if several transit modes are considered (for example, bus, LRT, subway, commuter rail etc), they are ranked in a way that if a combination occurs for trip between some origin-destination pairs (say bus with commuter rail) the lower-level mode has normally a shorter leg in the trip and is considered therefore as feeder mode. In most cases commuter rail can include any combination of other feeder modes and thus comes first in the hierarchy. Local bus is usually the lowest-level mode, while ranking LRT, subway, and intercity bus depends on the structure of the regional network. The higher-by-ranking modes include the lower-level mode lines in the transit assignment. This excludes the necessity of considering all the possible mode combinations explicitly. The availability of transit modes for trip between each origin-destination pair should be defined by analysis of the chosen paths in successive assignments starting with a full set of modes and then excluding the higher-level mode one at a time. The following example relates to three modes (bus, subway, and rail):

1. Path building in bus-subway-rail network; only for those OD-pairs that use rail this mode is considered available,

2. Path building in bus-subway network; only for those OD-pairs that use subway this mode is considered available,

3. Path building in bus network; only for those OD-pairs that use bus this mode is considered available.

The advantage of this definition of mode availability is that a full consistency between the mode-choice model and assignment simulation is guaranteed in application. Minor disadvantage is that there can be a clash (not really very often) between the network-simulated availability and the actually recorded mode in the survey (partially as a result of coding mistakes of origin and destination zones). Another minor problem is that some short trips can be converted to walks (even the whole half-tour can be converted to walk) though the motorized mode has been chosen for the trip or particular tour. The finer is the zonal system and the better is the transit network coverage and coding the less probability of things like that to happen. However, to a certain minor extent they are practically unavoidable in modeling networks, thus, some rules should be applied to treat these situations. The simplest rule is to create an OD-matrix of motorized transit accessibility indicators (1-transit, 2-walk, 3-inaccessible) at the trip level and use it in destination and stop-location choice in order to avoid conflicting situations (this point is not strictly applicable and requires some refinement if a fine geographical sub-zones are applied along with the TAZs). In particular if motorized transit is chosen as a main tour mode a possible set of stops should exclude trips between OD-pairs of categories 2’ and 3’. Accordingly, in the estimation procedure for stop-frequency and location models, intermediate stops for transit tours should include only stops made between motorized legs and not include stops made during the walk access or egress.



Table 5.2.3 below reflects the trip-mode availability rules subject to the tour main mode and trip place in the chain. Table 5.2.3. Tour and Trip Mode Combinations

Trip Mode Tour mode Place in the Chain 1. Drive alone

2. Shared ride (1+1)


4. Bus 5. Rail

Before interchange on outbound and after interchange on inbound

X 1. P&R

After interchange on outbound and before interchange on inbound

X (same for both half-

tours)

X (same

for both half-

tours) Before interchange on outbound X 2. K&R

After interchange on outbound and on inbound X X

No stops on the tour

X (same for both half-

tours)

X (same

for both half-

tours)

3. Transit with walk access and egress

At least one stop X X 4. Drive alone

X


X


X

The final decision on the set of modes can be made based on the typical (frequent) combinations relevant for the DRCOG region. A set of transit modes (currently including only bus and rail as examples) can be extended to include future modes. Usually, a lot of combinations can be discarded and only the frequent once are worth modeling. The MNL and nested structures will be statistically examined for both tour and trip levels. The following variables have generally been the most significant for both tour-level and trip-level mode choice models:

• Travel time and other LOS components (transfers), • Travel cost components including parking cost, • Distance, • Mode-income-specific constants, • Mode-car-sufficiency constants (including availability restrictions), • Mode-person-type constants (including availability restrictions), • Mode-destination-specific constants, • Stop-frequency (if not a part of alternatives’ definition in the joint structure). • Log-sums from the lower levels (possibly trip-mode choice log-sum in the main tour mode

choice).



Primary Destination and Stop Location Choices

Similar to the bi-level mode choice technique described in the previous sub-section, the destination choice model is essentially broken into two subsequent stages:

• Primary destination choice for the tour,

• Stop-location choice for each intermediate stop, conditional upon the chosen primary destination and stop frequency.

The primary tour destination choice structure is similar to the conventional trip destination choice structure with several modifications stemming from the accounting for both outbound and inbound directions of travel when calculating travel impedance. At this stage, the entire-tour is considered as a linked round trip and the choice of the destination is made across all traffic zones / access sub-zones in the modeled area conditional upon the known tour origin (home for home-base tours, workplace for at-work sub-tours, etc), tour purpose, household, person, and other variables. The following main explanatory variables should always be included into the zonal utility of the destination choice model (we also assume that the choice model is multinomial logit fully segmented by tour purpose):

• Size variable that is a total expected daily number of tours for the modeled purpose attracted to the sub-zone; size variable is logged in the destination choice utility expression and its coefficient was forced to be equal to 1.

• Mode-choice log-sum of the entire-tour level that is a composite utility across all modes available for travel from the anchor location to the destination and back to the anchor location. It serves as the most generalized impedance measure since it includes all time and cost components of the tour as well as other numerous perceived aspects of the travel (comfort, convenience, reliability) captured by the mode choice model coefficients. Mode-choice log-sums are calculated for the tour-specific combination of outbound and inbound periods if the TOD model is applied before the destination choice model. Alternatively, if the TOD choice is applied after the destination choice, predetermined representative TOD periods are assumed for each purpose and direction (for example, for work tours, AM peak period is assumed for outbound half-tours and PM peak period is assumed for inbound half-tours).

• Linear and non linear (frequently squared) distance terms (normally off-peak highway distance is used). These terms captures various non-linear impacts that cannot be modeled by mode-choice log-sum based on mode utilities that are linear with respect to the level-of service-variables. In particular, it helps capturing differential perception of extra time and cost for short and long journeys. In the model calibration, coefficients for distance terms can be adjusted to replicate the shape of the observed tour length distribution exactly.

The recent experience of development tour-based micro-simulation models for NYMTC and MORPC has shown that the new modeling framework allows for substantial improvement of the behavioral realism of the destination choice model by means of additional variables and model segmentations that were not available in the conventional trip-based format, but are readily available in the new framework. In particular, the following variables were tested and proved to be statistically significant being included into the destination choice utility function along with the three standard variables mentioned above:

• Additional perceived impedance associated with bottleneck facilities. For example, in the NYMTC destination choice model, three additional river-crossing dummies associated with the major regional watersheds – the Hudson River, East river, and minor (other) rivers from the North were added. These river crossings correspond to connections to Manhattan from three major sub-regions (1-New Jersey, 2-Queens, Kings, and Long Island, and 3-The Bronx and Upstate New



York) as well as for connection between them. These river crossings are associated with additional uncertainty and unexpected delays, so they are perceived by travelers as additional impedances above average travel time and cost components included in the mode choice log-sum. The travel time equivalent of these variables proved to be in the range of 20-60 min.

• Statutory borders (between states, counties, boroughs, and school districts). For example, intra-county dummy that is equal to one if the destination located in the same county as the origin was used in both the NYMTC and MORPC models. This dummy captures numerous important factors like familiarity with the area, local access to the information sources, location of public offices, etc, that are difficult to quantify directly. However, these factors are important in the destination choice process and they work in such a way that all else being equal, travelers strongly prefer locations for their activities from within the county of residence. The time equivalent of the intra-county dummy in the NYMTC model proved to be in the range of 30-50 min. For journeys to school, this dummy was replaced with a more specific intra-district dummy that corresponds to the borders of school districts in each county. These dummies serve as proxies for cognitive maps that have a strong impact on location choices made by travelers, but cannot be captured by physical measures of time, cost, and distance.

• Social frictions and income incompatibilities that play an important role for choice of work, school, and non-mandatory activity locations. In particular, in both NYMTC and MORPC models, destination choice for work tours was fully segmented by three income groups – low-income workers, medium-income workers, and high-income workers). These income groups are characterized by very different spatial structures of residential places and jobs as well as very different average commuting distances. It is also a known phenomenon that social and ethnic clusters of population play a significant role as spatial domains and affect choices of destinations for all types of activities. The last represents an interesting field for further research.

• Special attraction of destinations with high transit frequency and accessibility for captive transit riders. Captive transit riders (travelers under 16 year old and members of the households with low car ownership) proved to choose destinations with a better transit accessibility. This factor is partially accounted by the mode-choice log-sum (transit captive riders do not have drive-alone mode available); however, the associated destination choice effect proved to be even stronger than difference in mode choice log-sums. This variable was included in the MORPC destination choice model and proved to be an equivalent of 20-30 min of travel time.

• Available time window for implementing the tour. This variable is available in activity-based model systems like MORPC and ARC where tour generation and scheduling is conditional upon the generation and scheduling of the tours with higher activity priority. It is also used in time-space-prism modeling concept [Pendyala, 1998] where every person is explicitly tracked in time and space accounting for limited mobility. Narrow time window significantly reduces probability of implementing a long journey and in many cases physically constrains the set of available destinations.

Location choice for intermediate stops is similar to the primary destination choice in a sense that all factors and variables mentioned above including zone attractiveness, physically-measured and psychologically-perceived impedances, etc are equally relevant for stop-location choice as well. However, there is a principal difference in the way how stop-location choice is modeled since it is conditional upon the chosen primary destination of the tour. Thus, if in the case of primary destination, the choice utility is based on the impedance measure between the tour origin and alternative destinations; in the case of intermediate stop location, the choice utility is based on the relative deviation from the shortest path between the tour origin and primary destination that is necessary in order to visit the potential stop location.



Stop-location choice is closely intertwined with stop-frequency choice. There are two principal ways to design the model system with respect to the stop frequency and location choices:

• Consider stop-frequency at the tour-generation stage as a part of a daily activity pattern. This approach has an advantage of a better integration of tour and activity episode generation at the daily level. It was applied in the SFCTA and Portland METRO models.

• Consider stop-frequency at the tour-level sequence of choices between the entire-tour set of choices (primary destination, TOD, and entire-tour mode combination) and trip-level set of choices (stop location and trip mode). This approach has an advantage of a better integration of mode, destination, TOD, stop-frequency, and stop-location choices. It was applied in the NYMTC and MORPC models.

It should be noted that these two approaches can be combined in a flexible way that is included in the design of the ARC model system being developed currently. In both cases, however, stop-location choice modeling principles are similar. We illustrate these principles below base on the NYMTC stop-location model example when only one stop is modeled on each half-tour. The stop-location choice is modeled separately for each journey leg (outbound and inbound) where the stop frequency model yields a stop-making alternative. If the stop-frequency model does not indicate any stop-making the stop-location model is not applied for the corresponding half-tour. The main components of the stop-location choice model are presented in Figure 5.2.2 below. Effective rules can be applied to build a “spatial envelope” of potential stop locations that reflect traveler behavior in view of objective time-space constraints.

At Work

Stop-Location Choice Model

Choice Alternatives Structural Dimensions Utility Components

3-6 miles

3-6 miles

20-50%

Journey Purpose

Person Type

WorkSchoolUniversity

MaintenanceDiscretionary

Worker

Non-Worker

Child

Mode

SOV, Taxi HOV

Transit

Journey Leg

Outbound Return

Zonal Stop

(Size Variable)Attraction

CombinedImpedance

Route Deviation

Stop Activity

WorkSchool MaintenanceUniversity Discretionary

Figure 5.2.2. Stop Location Choice Model Components



Several principles for defining the spatial envelope form have been investigated (in all cases a spatial shape was built in order to cover at least 95% of the observed cases):

• A rectangular shape based on the absolute threshold for distance to the aerial strict line between the journey origin and destination,

• An ellipsoidal shape based on the relative route-deviation threshold,

• A double-circle shape based on the absolute threshold for distance to the closest journey end (either origin or destination),

• A bone-shape envelope depicted in Figure 5.2.2 that is a combination of the ellipsoidal and double-circle shapes.

The last form proved to be the most effective in terms of an area required to cover 95% of observations. he thresholds for the circle radii and maximum deviation from the shortest path have been established

cedure. An important adv a it covers both long and short journeys. For long journeys the t ourneys, route deviation can be significant in rela e sest end becomes an important limitation.

er-crossings, county borders and other real or perceived barriers to trav F ation-choice utility components (expressed in unit of distance) was applied and yielded reasonable results. It is based on the reasonable assumption that o nse should be similar to destination choice because stops are essentially secondary destinations and in many cases the definition of primary and secondary destinations of a u (especially for non-mandatory journey purposes like maintenance and discretionary).

Transit ssib ourneys. A zone is included into a tr en corresponding transit mode from both ication, a highway envelope (a lica for d destination pair associated with each journey. Then, a transit envelope is calculated for each transit mode as a subset of

, a

• combined distance through the intermediate stop ( ),

• absolute route deviation ( ),

• relative route deviation (

Tdifferentially for each of the six journey purposes as a part of the estimation pro

ant ge of the bone-shape envelope is that rou e deviation threshold is more important. For short j

he clotiv terms; however, an absolute threshold from t

In the original model formulation a simple highway distance was used as an impedance measure. This produced certain problem in the model application because it did not take into account additional impedances associated with major riv

el. inally, a scaled distance that had included all destin

stop-location choice in behavi ral se

jo rney was quite ambiguous

acce ility of intermediate zones was taken into account for transit jansit velope as a potential stop location only if it is accessible by the

the origin and destination zones. In both estimation and applpp ble rive-alone, taxi and shared-ride modes) is first calculated for origin-

zones from the highway envelope for the same origin-destination pair. Instead of the highway distancetransit in-vehicle distance is used. The following variables have been statistically examined in the utility function:

• Stop-attraction (zonal size) variable, • Combined impedance including intermediate stop, • Route deviation (either absolute or relative).

Several alternative measures for combined impedance and route deviation have been tested statistically

cluding the following basic forms: inj

kik

ijk ddd +=

ijjk

ik

ijk dddd −+=

ij

jk

ikij

k ddd

d+

= ),

where:



ijd = scaled distance between the journey origin and destination, ikd = scaled distance from the journey origin to intermediate stop, j

kd = scaled distance from the intermediate stop to journey destination.

After numerous statistical trials it was found that the relative route deviation measure performs better than absolute measure or combined distance for most travel segments. It means that when making decision about visiting secondary destinations on the way to the primary destination travelers estimate dditional disutility involved in raa ther relative terms (i.e. comparing to the travel impedance from the

e

hort absolute deviations. For short journeys (for such trav p

origin to the primary destination) than as an absolute quantity. In particular, this measure performs better for long journeys allowing for significant deviations in absolutterms. It has certain limitation for short journeys where a short base leg leads to significant relative deviations in combination with even comparatively s

el urposes as school and incidental shopping) absolute deviations is a better measure.



5.3. Activity Based Platform and Its Components Another important aspect of the proposed design alternatives for the DRCOG travel-demand system relates to the activity-based modeling platform. This includes several new modeling components that adifferent from the corresponding components of the conventional 4-step approach. These noveltiemostly reflect the last research and practical achievements in activity and travel modeling to describe different travel demand dimensions in a more coherent way including inherent i

re s

nter-relationship across different activity and travel-related decisions made by the same person or different members of the same household in a course of a day. The ultimate purpose of the new modeling approach is to better reflect the real decision-making process of individuals regarding travel choices including underlying behavioral stimuli and constraints. The momportant theoretical aspects that lay the foundation of the activity-based approach to travel demand

be summarized in the following way:

• Integrative modeling of activity and travel related decisions

st imodeling can

made by individuals by three following dimensions:

o Generation of activity episodes o Time allocation to various activity types o Location of activities and formation of travel tours to visit out-of-home activities

• Integrative modeling of individual person daily activity-travel patterns in the general framework of intra-household interactions that include the following dimensions:

o Explicit distinction between individual, joint, and allocated activities o Integrative framework of intra-household collective travel arrangements

Most of the contemporary research in the field of activity-based travel modeling as well as developed and e or several major aspects listed above. For example,

roach corresponds to an attempt to find an operation

e ime allocation that are relevant for trip and tour scheduling and duration modeling. These

e

Acti - trip / tour timing

o Explicit modeling of resource allocation (for example, cars) to the household members

applied regional model systems can be related to onoriginal individual daily activity pattern appframework for integrated tour formation and activity episode generation at the person level [Bowman, 1998; Bowman & Ben-Akiva, 1999, 2001, Bhat & Singh, 2000] that served as a basis for the Portland METRO [Bowman et al, 1998; Bradley et al, 1998, 1999] and SFCTA [Bradley et al, 2001; Jonnalaggadaet al, 2001] models. Numerous research works have been implemented in the field of time allocation to different types of activities [Borgers at al, 2002; Ettema et al, 2004; Fujii et al, 1999; Gliebe & Koppelman, 2002; Golob & MacNally, 1997; Goulias, 2002; Meka at al, 2002; Zhang et al, 2002; Zhang et al 2004; and Zhang & Fujiwara, 2004]. This branch of behavioral research provides important insights into factors that hav

pact on timworks are currently mostly academic and have not yet made the way through to the models applied inpractice because of the seeming incompatibility of continuous time-allocation units with discrete choicstructure of the regional models based on tours and activity episodes (stops). However, a welcome synthesis of the approaches is possible and has been already considered in the ARC model system design [PB Consult, 2004]. Time use and allocation aspect is closely related to the TOD choice that is an important travel dimension.

vity based platform offers a principally different approach to compared to conventionafactors” or tCambridge Sysincorporation of vity timing and duration choices. It also does not allow for explicit relation of different timing and duration choices made by individual in the

l 4-step models. Conventional 4-step models are mostly limited to either aggregate “peak ime-of-day choice models that operate with crude 3-4-hour intervals [Purvis, 1999,

tematics, 1999]. The framework of conventional 4-step models does not allow for variety of behavioral aspects pertinent to the acti



course of a .frequently emplscheduling modframework can mermans, 2000; Miller & Roorda, 2003] as weparticular fr2002]. A hybrid with 1-hour resolutio ign for ARC model syste Modeling in h ns and better understanding group-decision making mechanism have already attr ehavioral research community [Borgers at al, 2002; Ettema et a 0 2004; Golob & MacNally, 1997; Simma & Axhausen, 200 Sinteracti odel system as well as in the designed ARC model syst al, 2004a], explicit mod n o household members [V s Several first se rs and individu ca n, 1999, 2000; Mi usion of this important factor into the regional odesign. The sub-sec n ational model structure el framework.

Individ n

One of the m st imp tes to linking to , 1999, 2000 bly model and p dict travel decisions regarding number of tours, their destinations and modes when looking at omodelin l researc ach is that a daily activity set is a primary determinant of the

day Activity-based models are mostly based on a continuous representation of time and oys duration “hazard” modeling technique. Several comprehensive individual activity els that consider time allocation, activity sequencing and scheduling in the entire-day be mentioned [Pendyala, et al, 1998; Arentze & Tim

ll as numerous departure time and duration model for particular types of activities or agments of the daily activity agenda [Pendyala et al, 2002; Steed at al, 2000; Bhat, et al,

discrete departure time and duration model for scheduling travel tours that operates n has been applied in the MORPC model system and included into the propose desm [Vovsha & Bradley, 2004].

tra- ousehold interactioacted a significant interest from the bl, 2 04; Gliebe & Koppelman, 2002,

1; cott & Kanaroglou, 2002; Srinivasan & Bhat, 2004]. Several aspects of intra-household ons have been included into the MORPC m

em including coordinated daily activity patterns for household members [Vovsha et eli g of joint travel [Vovsha et al, 2003b], and allocation of maintenance activities t

ov ha et al, 2004b].

re arch works can be mentioned on explicit modeling car allocation to household membeal r availability in the context of activity generation and mode choice [Wen & Koppelma

ller et al, 2003]. They show constructive ways for incl m del framework that can be considered in the advanced stage 4 of the DRCOG model

tio s that follow give details on the corresponding activity-based approaches and opers that can be used in the DRCOG travel demand mod

ual Daily Activity Patter

o ortant enhancements made in the recent generation of travel demand models relaurs into an activity schedule spanning an entire day [Bowman, 1998; Bowman & Ben-Akiva; Bhat & Singh, 2000]. This stems from the recognition that it is impossible to reasonare

ne tour at a time and taking it out of the whole framework of the daily activity agenda. Many of the g principles applied in this modeling framework have adopted from recent activity and behaviorah. The basic paradigm of this appro

indi uvid al behavior while a set of travel tours is a derived entity to serve the desire to participatet s.

in activi ie There are im plications from this principle regarding the order of decision-making procedures al tools and models employed. In particular, different tour combination e ctivities should be modeled as more substitutable lower-level decision wh a tivities of a day should be considered as less flexible upper-level decisio integration of activity-based principles in operational travel dema m hieved by means of the following conceptual definitions:

• Acti

portant modeling imto model and the technics s rving the same basic set of aile dding or changing primary acn in the modeling hierarchy. The desired nd odeling framework has been ac

vity type that is defined by three main categories:

o Mandatory activity including work and studying in school/college/university; this activity by fixed participation and schedule; changing urban and travel

ity much. is usually characterized conditions does not influence this activ



o Household maintenance activity including shopping, personal business, escorting, eating, child care etc; this activity is usually characterized by quite fixed average participation, however, schedule is more flexible comparing to mandatory activity and changing urbanand travel conditions can rearrange it signi

ficantly.

o Discretionary activity includinetc; this a he most f

g leisure, entertainment, sport, visiting relatives and friends lexible in t ount of time spent a le;

ch r ndit n impa eipation and derived amount of travel for this category.

tivity

ctivity is tba

erms of amor

nd schedu acanging u n and travel co ions has mally a direct ct on th tivity

partic

• Ac location that is by two orie

o

defined main categ s:

In-home activo

ity. On-tour (out-o -home) activity

y activity set

f .

• Dail that is a list of activities in which the individual is inv co rse of a day ding in-home and on-tour locations.

ary activity

olved in the uinclu

• Prim of the daily activity set which is defined by the following rules:

o Mandatory activity is considered superior over maintenance and discretionary activity, ce activity is considered sup etionary

o re several activities of the same typ ctivity with the longest duration e nearest h is ed pr ary.

o several activities o p rounded to the nearest hour c ce er in-home activity.

o are several on-tour ac e same type and d the farthest y m e i

ac vel pa rn

maintenan erior over discr activity.

If there a e, the arounded to th our consider im

If there are f the same ty e and durationthe on-tour a tivity take pre dence ov

If there tivities of th uration,achieved b otorized mod s considered primary.

• Daily tivity-tra tte that is built as a set of tours based on a daily activity set with the win features:

o

follo g additional

All in-home activities are combined in one category without s y t e.

o lassified by the primary destination activity and se p frequency.

ubdivision b yp

Tours are c condary sto

o ut in sequenceTours are p (sc plementation in th a day.

• Primary tour

hedule) of im e course of

of the pattern that is a tour corresponding to the primar this activity is mary activity is in-home e patter does not have a primary tour. All other

are considered secondary

y activity if on-tour. If pritours

than th n.

ity- rn type• Activ travel patte that is defi ollowing primary ac n ina

o o Work at home,

School on tour,

ned by the f tivity and locatiocomb tions:

Work on tour,

oo School (studying) at home, o University on tour, o University (studying) at home, o Maintenance on tour, o Maintenance at home, o Discretionary on tour, o Discretionary at home, o Full day sick or absent from home.



To illustrate the introduced concept, we use the same three individual examples from Figure 5.2.1 above. However we attach hour to each activity, introduce in-home activity that is not shown in the figure andput them in schedule. Table 5.3.1 shows these details and summarizes the base defined terms (activiset, pattern and pattern type) for each of the individuals.

ty

able 5.3.1. Activity Patterns and Schedules T1st person 2nd person 3rd person Hour

Activity Tour/Journey Activity Tour/Journey Activity Tour/Journey Before 6.00 Sleep Sleep Sleep 6.00-7.00 Eat at home

Eat at home

Eat at home

7.00-7.30 Drop-off 7.30-8.00

Travel to work Work – outbound Travel to work

Work - outbound

In home discretionary

8.00-9.00 Travel to shop 9.00-9.30 Shopping 9.30-10.00 Travel to shop

Maintenance – outbound

10.00-11.00 Shopping 11.00-11.30 Travel to shop 11.30-12.00

Work at workplace

Shopping 12.00-12.15

Work at workplace

Travel to eat At work – outbound

Travel home

Maintenance – inbound

12.15-12.45 Eat at work Eat out Eat at home 12.45-13.00 Travel to work At work -

inbound 13.00-17.00

Work at workplace

17.00-18.00 Travel home Work – inbound Work at workplace

18.00-18.30 Eat at home Travel to shop

Study at home

18.30-19.00 Travel to shop Maintenance – outbound

Shopping Travel to eat Maintenance – outbound

19.00-20.00 Shopping Travel home

Work – inbound

Eat out 20.00-20.30 Travel home Maintenance –

inbound Travel home Maintenance –

inbound 20.30-21.00 21.00-22.00

At home discretionary



After 22.00 Sleep

Sleep

Sleep Activity set Maintenance

Work Maintenance Maintenance Discretionary

Maintenance Maintenance Work Maintenance Maintenance Discretionary

Maintenance Discretionary Maintenance Maintenance Maintenance Maintenance Discretionary

Primary activity

Work Work University

Primary tour Work Work Pattern type Work on tour Work on tour University at home

Work tour – primary Work tour – primary with stops on both journeys

Maintenance tour – secondary with stops on both journeys

Activity-travel pattern

Maintenance tour-secondary At work sub-tour – secondary Maintenance tour - secondary Some additional details can be added to the proposed conceptual framework based on the statisticaanalysis of travel habits in the region. For example, definition of activity-travel pattern type can be extended to incorporate a configuration of primary and secondary tours. This will require an additional straightforward definition of the secondary and tertiary activities. However, statistical analysis in several metropolitan areas (New York, Portland, and San-Francisco) has shown that daily patterns with three or more tours are not frequent, thus, in most cases this extension would merge pattern type with pattern self. Another important detail relates to the intra-household interactions amon

l

gst different members scussed below.

itthat will be di



Application of the proposed conceptual framework for operational modeling is shown in a general way in Figure 5.3.1 below. It is expressed in a rational hierarchy that corresponds both (meaningfully) to the underlying decision-making process of individuals and (formally) to the sequence of models applied. urther on it will be combined with the other type of decisions – strategic long-term decisions that recede the individual pattern and detailed travel-related decisions that follow it.

Fp

Daily Daily-TravelActivity Activity

Set

Primary

Pattern

Activity

Secondary

Pattern typeAtHome

Activities

Primary TourConfiguration

Secondary ToursConfiguration &

OnTour

AtHome

SequencingOnTour

PrimaryDestination

PrimaryDestination

SecondaryStops

SecondaryStops

Figure 5.3.1. Individual Daily Activity Travel Pattern

avel demand models operate with a limited number of choice objects and subjects. In part la t trip distribuNumeromembe The rosubjects person-operativ reates an important hierarchy that reflects real-world decision-making rules and also serves as a powerful tool for the d

Different Levels of the Choice Hierarchy

The conventional tricu r, a trip production model is usually applied at the household-day level, while the subsequen

tion, mode choice and time-of-day models are applied to the separate person-trip units. us important linkages across trips made by the same person or across several household rs are lost in this simplification.

p posed modeling framework allows for consideration of various decision-making objects and in a more complete and flexible way. It includes several new units like household-day andday as well as distinguishes between long-term decisions, decisions made on the daily basis and e decisions made in a limited time window of several hours. This differentiation c

mo el system decomposition into a sequence of manageable sub-models.



Figu 5 ls reflects vel choice is conditional on the upper-level cho s

re .3.2 shows the base levels of decision-making and modeling hierarchy. The sequence of modethe order of modeling choices where each lower-le

ice .

Residential place

Regular workplace/school/university

Auto ownership

Individual activity/travel pattern type

Household joint tours

Individual daily activity-travel pattern

Entire-tour TOD

Pre-mode (motorized/non-motorized)

Primary destination choice

Entire-tour main mode combination

Stop frequency

Stop location

Detailed trip mode

Detailed trip TOD (departure/arrival)

Household

Lon

g-te

rmD

ay

Person with intra-household linkage

Household

Person with intra-household linkage

HouseholdPerson with intra-household linkage

dow

e w

in

Tour

Su

cces

sive

ly-n

arro

wed

tim

Journey

Trip

From th

Figure 5.3.2. Decision Making and Modeling Hierarchy

e time horizon perspective there are three distinctive types of choices:

• Long-term choices that include residential place and regular places for mandator(work, school, university) of the household members. They also inclu

y activities de auto ownership as a

f vities. Locations of the place of re ts that constrain a

• E

unction of the household characteristics and locations of the base actisidence and mandatory activities serve as important spatial pivot poin

ll other household and person activities.

ntire-day choices that relate to the activity travel patterns of the household members. As a

l one and less dependent on the household composition. Then joint household tours should be modeled because they mostly take precedence over secondary individual activities. Finally at this

rn is modeled for each person where secondary tours s a aro indi nd joint household tours.

s made in the available

result of this sub-sequence of choice models a set of ordered (sequenced) tours is generated for each household member with details regarding presence of intermediate stops. The proposed decomposition rules based on statistically checked assumption that activity-travel pattern-type decision that relates to primary tours (mostly mandatory activities) is mainly an individua

sub-sequence the whole individual patteand activitie

l choice

re scheduled und primary

time window

vidual a

• Trave that is gradually narrowed down with all ecision m de. This modeling principle avoids a serious deficiency of the subsequent d a



conventional demand models that allo or conflictin mes chosen for different trips of the dividual. The micro-simulation sup rts explicit tracking time-use components

of the travel choices in such a way that each individual has an available time window at each micro-simulation stage. Initially each individual has a full-day open window. Then, starting with

w f g tisame in framework po

primary tours and mandatory activities and going down the predetermined sequence of tours, this window is narrowed by the previously made TOD choices.

From the choice object and subject perspective there is a correspondent transformation through the following steps:

• Entire-household level that relates to such choices as residential place, auto ownership and number of joint tours made by several household members traveling together. These choicebe attributed only to the entire household and modeling them requires consideration of entire-hous

s can the

ehold composition rather than individual characteristics.

• Person level that relates to such choices as regular location of mandatory activity (workplace, school, university), daily pattern type and detailed structure by tours. Though the underlying decisions are mostly individual, there is a comparatively strong linkage across household members (though not enough strong to justify an entire-household formulation). This linkage is modeled based on the intra-household priority rules that are discussed further.

• Entire-tour level that relates to each tour of each person modeled one at time in sequencecovers TOD choice, pre-mode choice between motorized and completely non-motorized touprimary destination choice, main mode combination for the tour (if motorized mode is chosen) well as exact number and p

. It rs,

as urpose of intermediate stops on each of the journeys (half-tours).

• Half-tour (journey) level that relates to modeling locations of each stop conditional upon the known origin and primary destination of the tour.

• Elementary-trip level that relates to the detailed mode and timing choices conditional upon the chosen mode combination and TOD for the entire tour as well as for the previously modeled of the same tour.

Conditioning the lower-level choices on the upper

trips

-level outcomes ensures a consistency of the detailed low eved in the conventional trip-based modeling framework. For exa e ttributes of the trips in the same tour (including non-home-based trip r ical conflicts because they are conditioned on the entire-tour attr e across choice hierarchy reflecting important fact that reas ould be based on the estimation of spectrum of the nd nt aspect is discussed below in the sub-section conceptual

segmentation of household and person activities is used:

er-level decisions that cannot be achig ampl , all location, mode and timin

s) a e linked in a way avoiding logibutes. There is also and upward linkagonable prediction upper-level choices sherlying lower-level choices. This importau

lines of the model system integration. Additional important aspect of the day-level tour / activity generation hierarchy relate to the classification of tours / activities by principal types and settings [Vovsha et al, 2003a]. In the design and development of the MORPC and ARC modeling system, the following three-part

• Individual activities. Corresponding tours are generated and scheduled at the person level (with possible inclusion of the household variables, but without direct coordination of choices). The

frequency of these activities is modeled for each person either as a part of the daily activity/travel pattern (as currently proposed), or by means of the frequency choice model.

• Allocated activities. Activities are generated at the entire-household level because they reflect collective household needs. However, they are implemented and scheduled individually. Thu

the s,



an activity (or tour) frequency model is used for the household, followed by an intra-househoallocation model that household members as alternatives.

ld

• Joint activities. Corresponding tours are generated at the entire-household level and also implemented by several household members traveling together (and frequently sharing the same

activity). A tour-frequency model is used for the household, followed by a person participation model that is applied for each generated tour and considers possible travel parties (subsets of the household members) as alternatives.

The activity types and trip purposes are grouped into three main segments:

• Mandatory activities (including going to work, university, or school).

• Maintenance activities (including shopping, banking, visiting doctor, etc).

• Discretionary activities (including social and recreational activities, eating out, etc). Table 5.3.2 summarizes the main assumptions made regarding the possible combinations of activity typeand settings. Only five out of the nine possible combinations are allowed, which greatly simplifies the modeling system, while preserving behavioral realism and covering most of the observed cases.

Table 5.3.2. Modeled Activity-Travel Segments

s

Activity Type / Travel Purpose

Individual Setting

Allocation Setting

Joint Setting

Mandatory X Maintenance X X Discretionary X X

Travel for mandatory activities is always assumed to have an individual character. Frequency of these activities, location, and scheduling are modeled for separately for each person. While household-composition variables are used in the utility functions for these individual activities, there is no explicit linkage across all choices made by different individuals with the notable exception of staying at home together or having a non-mandatory travel day together. This assumption is based on the fact that most of the mandatory activities have fixed frequencies and schedules defined exogenously to the household activity framework; however, a realistic activity-based model should be sensitive to the fact that unscheduled at-home activity (child at home sick) will negatively impact the frequency of other mandatory travel. Maintenance activities may be either allocated or joint. It is assumed that the maintenance function is inherently household-based, even if it is implemented individually or related to a need of a particular household member, like visiting doctor. Even in these cases, maintenance activities are characterized by a significant degree of intra-household coordination, substitution, and possibly sharing. Discretionary activities may be either individual or joint. It is assumed that these activities are not allocated to household members since they do not directly relate to household needs. Thuactivities are either planned and implemented toge er by several household members or are planned and implemented individua

is assumed that all else being equal, there is a predetermined structure of preferences in the activity

s, these th

lly. Itgeneration and scheduling procedure along both dimensions (activity type and setting). Mandatory activities take precedence over maintenance activities, while maintenance activities take precedence over



discretionary activities. Joint activities are considered superior to allocated activities, while allocateactivities are in turn considered superior to individual activities. Combination of these two preference principles yields the following order of generation and scheduling activities that serves as the main modeling skeleton for the model system design:

1. Individual mandatory activities, 2. Joint maintenance activities,

d

4. 5.

In t Mgenerati n of joint articipation models for joint and allo e

Indivi

This aspmodels model destination and mode of each trip sep t

3. Joint discretionary activities, Allocated maintenance activities, Individual discretionary activities.

he ORPC and ARC model system the stages 2, 3 and 4 are partially combined. The household on of all joint tours may be done in one simultaneous choice structure and a further combinatioand allocated tour generation stages is considered. The person p

cat d tours, however, are still de-composed into stages 2-4 and implemented sequentially.

dual Time-Space Constraints and Time-Use Variables

ect relates closely to the choice hierarchy described above. The conventional travel demand ignore this important aspect completely because they

ara ely and independently of each other. Moreover, because of the fractional-probability outcome of each of the choices it is practically impossible to control feasibility of the modeled alternatives for each individual. Micro-simulation framework opens a way to solve this fundamental problem and introduce

al realism to the modeled travel choices of each individual.

lustrates a concept of individual time-space constraints for several modeling steps in a

behavior Figure 5.3.3 ilsim ie(before When it ork tour (high-priority mandatory activity) is processed first and the whole-d t ing pattern (AM for the outbound jo e OD choice there is no restrictio n s (even very distant from eestablished t uting distance or time, that is usually not a case in metropolitan areas). After the de na ur is chosen, the maintenance tour is modeled that is

heduled after the work s only a comparatively narrow time window left within e AM period (6.00-9.00). Taking into account that the work tour should start before 10.00 and the

ance activity itself lasts at least several minute s no possibility to reach destinations that require more than hour and a half travel. Thus, all the destinations that require travel time longer than that by the fastest available m pplication stage. Thus, pplication of the time-space constraint rules introduces behavioral realism to the modeling procedure

plif d way. Consider a person who has to implement two home-based tours – work and maintenance work) without intermediate stops – as a result of the activity-travel pattern choices.

comes to the TOD choice the way ime window is still open. Assume that the most frequent work-timurn y to work, PM for the inbound journey to work) is chosen. For this Tn o the spatial availability of destinations, thus virtually all regional destination th residential place) should be considered (if there is no absolute regional threshold on he comm

sti tion and mode for the work totour. For this tour there isc

thmainten s there i

ode should be excluded at both estimation and aaand avoids numerous internal conflicts across TOD and destination choices that are inherent in the

ilable e models leads to heavily biased estimators for

modeled parameters that distort the model predictions for “right” available alternatives as well.

conventional modeling framework. It should be noted at this point that inclusion of “wrong” unavaalternatives in the estimation procedure of the choic



AM Midday PM Night

6.00 9.00 15.00 19.00 Time

Distance

Work tour TODMaintenancetour TOD

150

75

Maintenancetour

availabledestinations

Work touravailable destinations

0

Figure 5.3.3. Individual Time-Space Constraints

ORPC and ARC model systems, explicit use of residual time windows left after scheduling toured with activities of higher priority as variables for subsequent generation and scheduling of the urs serves as one of the most important factors ensuring integrity and consistency of the al activity patterns and schedules. The general time windowing

In the M s associatother toindividu rules that have been applied are

d below. The TOD choice model applied in the model system has a temporal resolution of one his enhanced temporal resolution o

describehour. T pens a way to track and schedule explicitly all tours and activities implemented by a person in the course of the day. The following definitions and rules have been accepted for explicit modeling the temporal dimension – see also Figure 5.3.4 below:

• Full formal window that includes 19 hours from 5AM to 23PM. All activities before 5AM are shifteto 5. All activities after 23PM are shifted to 23. Tours can be scheduled within the full formwindow back to back; however they must not have overlaps or spa

d al

n each other.

• Tour time-of-day (scheduling) correspond to the departure from hhour. It includes activity duration and travel time both from home

ome and arrival back home to the activity and from the

activity back home as well as all intermediate stops on the way.

• Active window includes only 16 hours from 7AM to 22PM (i.e. excludes extremely early or late hours 5, 6, and 23). Activity window is used for calculation of residual time windows and their overlaps as variables fed to the tour generation models. Though tours may be scheduled within the full formal window, the extreme hours are highly infrequent for starting non-mandatory activities. Thus, truncated active window proves to be a better parameter that characterizes person time resources for considering a new activity.

• Residual person time window is calculated as the number of hours left open within the active window after scheduling some activities. In the tour generation procedure residual windows are calculated once after scheduling mandatory activities. In the time-of-day choice model, residual



windows are updated sequentially after scheduling each mandatory or non-mandatory tour and used to generate feasible time-of-day alternatives for the subsequent tour.

• Residual window overlap is calculated as a number of mutual hours left open in the active

adults

windows of two persons. Window overlaps represents important measure of potential for joint activity/travel. There are several pair-wise window overlaps specific to person types intensively used in the joint travel model:

o Maximum pair-wise window overlap across the household adults o Maximum pair-wise window overlap across the household children o Maximum pair-wise window overlap of the household adults and children o Maximum pair-wise window overlap of a particular person with the (other) household

o Maximum pair-wise window overlap of a particular person with the (other) household children

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Person 1

Person 2

Full formal window for scheduling toursActive window for generating new tours

Work tour 6-16 Residual window17-22 (5 hours)

Work tour 7-14 Universitytour 16-19 20-2215

Residual window (4 hours)

Persons1 and 2

Overlap20-22(3 hrs)

Figure 5.3.4. Time Window Rules (MORPC, ARC)

This sort of eff ropose to xplicitly include the following types of constraints:

ects can be further extended to other modeling dimensions. Overall we pe

• TOD-period-level constraint on scheduling activities that avoids conflicting overlaps, that is each successive tour (or trip within the tour) cannot start (departure period) in an earlier period than the end of the previous tour or trip (arrival period); note that there can be several successive tour or trip ends within the same period.

• Available destination radius for the primary tour destination based on the maximum distance (travel time) by the fastest mode available for the person within the time window available for the tour.



• Maximum route deviation for intermediate stop location based on the tour origin, primary destination, and maximum distance (travel time) by the mode chosen for the tour within the twindow available for the corresponding journey (half-tour).

• Hour-level

ime

constraint on trip departure and arrival time choice that avoids conflicting overlaps for each successive trip (within tours and across tours) in a similar way to the TOD-period-level

on scheduling but with a finer tempoconstraint ral resolution.

• Private auto availability for non-home-based tours (at work) based on the chosen mode for the corresponding work tour. Private auto mode is supposed available for the person at work only if

•

the mode for journey to work was drive alone or shared ride as a driver.

Mode consistency across trips within the tour based on conditioning of trip-mode choicesupper-level entire-tour mode-combination choice and on each other. In particular, drive alone on particular trip is prohibited if it is not chosen as the main tour mode and transit modes on both half-tours are conditioned in such a way that they can be different (say commuter rail on the wayto work, while bus on the way home) only if there are intermediate stops or network changes that can approve that switch.

Real-time individual au

on the

• to availability for each person and tour that is based on the intra-

and

rship ing and

household priorities across the household members and scheduled tours. This option requires each household automobile to be explicitly monitored during 24 hours including its allocationuse by household members and tours. The research implemented on this issue has shown that real-time individual availability of auto is a much better explanatory variable than a simple household auto ownership. This option is extremely effective if the household auto owneincludes a vehicle type dimension. Though this option introduces an additional modeltechnical complexity it constitutes a significant step forward in better modeling detailed vehicle-type flows. It should be noted that in many respects (for example environmental or traffic engineering studies) travel demand modeling is only a premise for the vehicle flow estimation. Thus, the usual accent made on travel-demand segmentation whereas vehicle-type segmentationis ignored is not quite justified.

f these constraints like the TOD-period-level scheduling, maximum route deviation and mode-onsistency for work and at work tours are fairly simple and can be in

Some o

oice c cluded into the stage 1 of

dimension exi es are m e ls account f

chthe model system development. Some other ones like individual car availability require additional modeling efforts and can be implemented only at the advanced stage 4.

Operational Classification of Intra-Household Interactions

Another known drawback of the conventional demand models is that they almost completely ignore the numerous interrelationships across household members. In most conventional models entire-household

sts only at the trip-production stage while all time-scheduling, mode and destination choicod led separately by persons and trips. Inclusion of household-related variables into these mode

or the intra-household linkages to a certain extent, however the micro-simulation environment creates all the necessary prerequisites to explicitly model important intra-household linkages in full.

iously publis The prev hed research works were mostly focused on time allocation aspect and less on generatioEttem e 02; Meka at ang et al 2004; and Zhang & Fujiwara, 2004 ive examples of models for time allocation between various type of activities and household members.

crete

units of travel and discrete choice modeling technique.

n of activity episodes, trips, and travel tours. In particular, the works of Borgers at al, 2002; a t al, 2004; Fujii et al, 1999; Gliebe & Koppelman, 2002; Golob & MacNally, 1997; Goulias, 20

al, 2002; Townsend, 1987; Zhang et al, 2002; ZhgThough these works provide valuable insights into the intra-household decision-making mechanism theyare not directly compatible with the structure of most travel demand models that are based on dis



Most of the approaches including Borgers at al, 2002; Ettema et al, 2004; Gliebe & Koppelman, 2002, 200 t, 200 Thousehostructurextendein order The adomechan

4; Golob & MacNally, 1997; Simma & Axhausen, 2001; Scott & Kanaroglou, 2002; Srinivasan & Bha4; ownsend, 1987, were limited to household heads only and did not consider explicitly the other

ld members as active agents in the intra-household decision making. In most cases, the model e was essentially built on the assumption of a “binary” household and could not be easily d to incorporate more than two interacting agents. This is another limitation that has to be lifted to integrate intra-household interactions in the framework of regional travel demand models.

pted operational structure of intra-household interactions distinguishes between two principal isms – activity coordination and resource allocation – see Figure 5.3.5 below.

Activity Coordination Resource Allocation

Entire-Day Level

Coordinated DAP type

Joint Activity-Episode Level

Task Allocation Level

Allocation of carsto household

members

JOINT non-mandatory activity

RIDE sharing for mandatory activity

ESCORT children

Mandatory Non-Mandatory Home

ALLOCATED maintenance task

Figure 5.3.5. Operational Classification of Intra-Household Interactions

ty coordination mechanism

he activiT reflects the way household members interact in order to undertake

various joint activities and/or travel arrangements as well as allocate household maintenance tasks to household members. It is based on the general behavioral phenomenon that joint participation in ctivities has an added “group-wia se worth” that cannot be reduced to a simple sum of individual utilities

for each participant. It is also represents various compromises made by some household members in order to serve the other household members representing “altruistic” behavior that cannot be explained by the individual utility maximization. Activity coordination is the focus of the current paper. Resource allocation represents another facet of intra-household interactions. Even if activity agenda of all household members on a given day includes only “pure” individual activities, they have to interact in order to allocate constrained resources between them. In the context of travel demand modeling, the most important allocated (and frequently constrained) resource is household cars. First attempts to



incorporate intra-household allocation of cars as a part of a travel demand model have been made by Wen & Koppelman, 1999, 2000 and Miller et al, 2003. ctivity coordination mechanism can be stratified by three following principal layers of intra-household teractions:

Ain

1. Coordinated principal daily pattern types at the entire-day level. We consider three principal dpattern types: (1) mandatory (work, university or school activities, which might include additional out-of-home non-mandatory activities); (2) non-mandatory travel (only non-mandatoryactivities at least one of which is out of home); and (3) staying at home or absence from town for the entir

aily

e day. Statistical evidence shows strong coordination between household members work

er for major shopping trips, family

2.

at this principal level, resulting in such decisions as staying home for child care; coordinated commutes; and household members taking time off togethevents and vacations.

Episodic joint activity and travel. Even if household members have chosen different pattern types

3.

(for example, one mandatory and the other non-mandatory) they may participate in shared activities and/or joint travel arrangements. We propose a classification of typical joint activity and travel types that support the development of operational choice models. In particular, we distinguish fully joint travel tours for shared activities from partially joint tours, in which household members share transportation without participation in the same activity.

Intra-household allocation of maintenance activities. Many of the routine household maintenance activities (shopping, banking, visiting post office, etc) are implemented and scheduled individually; however, generation of such an activity and its allocation to a particular household

ehold interaction mechanism to be properly understood and modeled. Maintenance

task allocation mechanism may not be observed completely within a one-day framework since

It is also assumed that all else being equal, a general hierarchy of intra-household decision making ttom. It means that entire-day level decisions come first. Then,

ch household member, the decisions regarding joint

of a

member is a function of a household decision-making process. Thus, these activities require anintra-hous

most of the maintenance tasks have cycles longer than one day.

follows these three layers from top to boconditional upon the chosen daily pattern types for eaactivities and travel are made. Finally, maintenance activities are allocated to persons conditional upon the chosen daily patterns and participation in joint activities. These assumptions give a schematic andsimplified view on the extremely complicated real-world variety of travel behavior of the members household and numerous interactions between them. This view, however, has two important features:

• The proposed structure gives a good coverage for most frequent cases of intra-household interactions observed in the household travel surveys; also many complicated cases of joint activities and travel arrangement that do not fall directly under one of the proposed categories,

d still should be only slightly simplified or split in order to be brought in line with the proposestructure.

• The propose structure serves as a constructive framework for derivation of operational choice models that can be estimated based on available household travel surveys and applied in a framework of regional travel demand models.

Further classification of episodic joint activities is subject to the purpose of travel demand modeling. At this stage we do not model explicitly in-home activities. Thus, only out-of-home activity episodes associated with travel are analyzed and classified. The following principal categories of episodic joint activity and travel are distinguished – see Figure 5.3.5 above:

1. Joint travel generated by the shared activity. This category is almost exclusively bound to non-mandatory activities (shopping, eating out, other maintenance, and discretionary activities) as



well as almost exclusively implies a fully joint tour structure. Cases where fully joint tours include an escorting function (recorded drop-off or pick-up purpose for one of the household members) have been assigned to the escorting category. Cases where fully joint tours include mandatory activity (work, university, school) have been assigned to the category of ride-sharing for mandatory activity. In the last case, joint travel occurs as a result of time-space synchronization of individual activities and implies only a travel co-operation without sharing the activity. Thus, the modeling technique for synchronized mandatory activity cannot employ a joint destination choice and time-of-day scheduling as in the case of shared activity, but rather should link individually made choices in time and space. Rare cases where activities reported by different members of the travel party within the same fully-joint tour proved to be different (say shopping and discretionary) were considered as individual tours.

2. Joint travel to synchronized mandatory activities. This category has a significant share of drop-offs and pick-ups of school children made by workers on the way to and from work. Additionally, a significant percentage of school children travel together to and from school generating fully-joint tours and joint half-tours. Also carpooling of workers for commuting to work is observed, though this type has a comparatively low percentage. Even going to the same school is not considered as shared activity because it finally has an individual character and joint travel lasts as long as there is a time-space synchronization of the individual activities.

3. Escorting that is the purely “altruistic” purpose of driving some other household member without participation in the activity. Statistical analysis has shown that majority of escorting is associated with serving children who cannot drive alone and, in the case of preschool children, cannot even ride transit alone.

The ultimate purpose of incorporation of intra-h ehold interactions in the framework of a regional demand model, dictates intra-household interactions:

• Full coverage of all household, person, and activity types

ousseveral necessary requirements to the models of

; from this perspective certain simplifications of the modeling structure would be acceptable, rather than exogenous segmentation by household types, person types, or activities.

• Compatibility with the tour-based model structures that are used for modeling destination and mode choices; in particular in many cases tours and half-tours (outbound and inbound) are used as units of decision making rather than activity episodes or durations (time allocations).

• Compatibility with the micro-simulation modeling paradigm that requires probability distributions to be associated with the ranges of modeled parameters; for this reason discrete choice structures are preferred to models that operate with continuous average values.

A general framework for incorporation of intra-household interactions in a travel demand model system is shown in Figure 5.3.6 below. A fully-fledged regional travel demand model system includes all types of joint, allocated, and individual activities in combination with the corresponding travel. From this point of view, the system of intra-household interactions described in the previous section and the five choice models resulting from the adopted assumptions constitute the upper-level activity generation set of models. They are followed by a set of models for non-mandatory individual activities (individual mandatory activities are modeled first as a part of a daily pattern choice). In this framework it is assumed that decisions made on individual non-mandatory activities are conditional upon the decisions made regarding mandatory and joint activities.



Household Person Activity/tourDAP

Generate

JOINT

RIDE

ESCORT

ALLOCATED

Coordinate

ate

Generate

Generate

Locate /ScheduleMandatory

NM

CoordinHome

Generate Participate

Link Participate

Child

Chauffeur

Demand

Allocate

Pool

Generate Allocate

INDIVIDUAL Generate Locate /Schedule

Locate /Schedule

Locate /Schedule

Figure 5.3.6. Intra-Household Interactions in a Travel Demand Model System The micinteract s arises fr re househo nes at the level of persons. In particular, the following typical mechanism can be menti

• ividual choices made by each person.

• ; choice to participate formally relates to the

•

e activities together (if there are several demands at the same time)

The next sub-sections give examples of already developed and used models for various types of intra-household interactions in the framework of NYMTC and MORPC regional models.

ro-simulation framework allows for explicit modeling of individual households and persons as ing agents. A constructive and operational approach to modeling intra-household interactionom modeling activity generation process at different levels – in some cases at the level of entild and in some other o

oned:

Daily activity pattern type choice relates to persons and the household level serves only for coordination of ind

• The decision-making unit for non-mandatory joint activities is assumed to be a household while person decisions relate to participation in joint activities.

The interaction mechanism for ride-sharing for mandatory activities is different from the first twothese activities have already been generated and the person; however linkage of travel tours of different persons occurs at the household level.

The interaction mechanism for escorting children is also unique; demand for escorting comes from the individual activities (mandatory and non-mandatory) generated at the person level; then (possible) pooling of thesand allocation to the “chauffeurs” is implemented at the household level.

• Finally, maintenance tasks are generated by the entire-household needs and then allocated topersons.



Linked Tour-Frequency Models

o effects – person time-space

here are three person types (worker, non-working adult and child), and six travel purposes (work, ersity, at work, maintenance, discretion y), finally yielding 13 journey-frequency models

shown in the Figure 5.3.7 below. This takes into account that children do not travel to work, at work and to university; and that n tours.

Linked tour-frequency models represent an example of a combination of twconstraints and intra-household interactions – adopted for the NYMTC model for the tour generation stage. Though explicit formulation of the individual daily activity-travel pattern described above is theoretically appealing and superior to the set of tour-frequency models, the last option is technically simpler and gives a good approximation to the daily patterns for all household members. This structure is proposed for the stage 1 of the DRCOG model system development. Tschool, univ ar

on-working adults cannot implement work and at-work

Int

Workers Non-Workers Children

1.School

Man

dato

ry 3.School2.School 5.University 4.University

6.Work

7.At Work

inte

nanc

Ma

e

9.Maint

8.Maint

Dis

cret

iona

ry

10.Maint

ra-Household Interactions

Ti

Indi

vidu

alm

e-Sp

ace

Con

stra

ints

11.Discr

12.Discr

13.Discr

gure 5.3.7. requ Fi Linked Journey-F ency Models



A he tour-frequency models is ordered and linked in the following way:

1. Tours to school for children,

2. Tours to school for workers, having a child-at-home Boolean indicator that has a negative impact

andatory-tours made by the same worker that have a negative impact on tour-making,

9. e, other-mandatory-tours made ve

10. e

g ntra-household co-operation in mutual tour-making) and tours at work made by

11. ther-same non-worker (these have a negative impact

12. -gative impact on tour-making)

ct on

13.

ll the in or tour purposes proved to be very

-

set of t

on tour-making,

3. Tours to school for non-working adults, having a child-at-home Boolean indicator that has a negative impact on tour-making,

4. Tours to university for non-working adults, having a child-at-home Boolean indicator that has a negative impact on tour-making,

5. Tours to university for workers, having a child-at-home Boolean indicator that has a negative impact on tour-making,

6. Tours to work, having indicators for child-at-home event and other-m

7. Tours at work for those workers that made a tour to work,

8. Maintenance tours for non-working adults, having indicators for child-at-home event, other-mandatory-tours made by the same non-worker (these have a negative impact on tour-making) and tours to work made by working household members (those have a positive impact reflecting that non working adults usually take care on shopping, children’s chaperoning and other household-maintenance needs from workers),

Maintenance tours for workers, having indicators of child-at-homby the same worker and maintenance tours made by non-working household adults (these haa negative impact on tour-making),

Maintenance tours for children, having indicators for school tours made by the same child (theshave a negative impact for tour-making) as well as for maintenance tours made by adult household members – working and non-working (those have a positive impact on tour-makinreflecting iworkers (these have a negative impact on tour-making reflecting a maintenance purpose for majority of at-work),

Discretionary tours for non-working adults, having indicators for child-at-home event and omandatory-and maintenance-tours made by the on tour-making),

Discretionary tours for workers, having indicators for child-at-home event and other-mandatoryand maintenance-tours made by the same worker (these have a neas well as for discretionary tours made by non-working adults (those have a positive impatour-making reflecting intra-household joint tours),

Discretionary tours for children, having indicators for school and maintenance tours made by the same child (these have a negative impact on tour-making) as well as for discretionary tours madeby working and non-working adults (those have a positive impact on tour-making reflecting intra-household joint tours).

dicators providing the linkage across household members Asignificant statistically. Thus, both intra-household interaction (horizontal linkage) and individual timespace constraints (vertical linkage) have been recognized as important factors in household tour making.



Coordinated Daily Activity Patterns of Household Members

The more advanced model systems for MORPC and ARC follow the multiple layers of intra-household interactions shown in Figure 5.3.5 above. The upper level of intra-household interactions relates to coordination of principal daily pattern (DAP) types of all household members. Figure 5.3.8 below shows the structural dimensions along which DAPs are classified in the current modeling system for MORPC (two upper levels of the structure).

Daily activity-travel pattern

Non-mandatoryMandatory At home /

absent

Workday University day School day

Worktours

Work &University

Universitytours

University& Work

Schooltours

School &Work

1 2+ 1 2+ 1 2+

1 2 3 4+

Secondary tour configuration for mandatory pattern

None Before After Both

Current version

Possible enhancements

Tourfrequency

Activitytype

Frequency of intermediate stops on tours

Figure 5.3.8. Classification of Daily Activity Patterns DAP is classified by three main types:

• Mandatory pattern that includes at least one of the three mandatory activities – work, university,or school. This constitutes either a workday or a university/school day, and may include additional non-mandatory activities such as separate home-based tours or intermediate stops on the mandatory tours.

• Non-mandatory pattern that includes only maintenance and discretionary tours.

At-home pattern• that includes only in-home activities. At the current stage of model development, at-home patterns are not distinguished by any specific activity (work at home, take care of child, being sick, etc). Cases with complete absence from town (business travel, vacations) were combined with this category.



Further on, mandatory DAPs are classified by purpose and frequency of mandatory tours. The vast majority of observed cases include only one or two tours, where two-tour combinations include either two tours to the same primary activity or a combination of work and university/school activities. The DAP frequency has been calculated based on 13,534 person-days included the 5,555 households xtracted from the Mid-Ohio Household Travel Survey, 1999. The distribution of observed DAPs is shown

ntly employed for segmentation in the model e and application. Table 5.3.3. Obs e y of r D Pe atego

D acti e

ein Table 5.3.3 below for the seven person types subsequestimation

erved Fraily

quencvity patt

DAPs forn type

ifferent rson C ries

Mandatory travel Workday University day Schoo ay l dWork tours University

tourSchootours s

l

1 +

U

1 2+

U&W

1 +

Non-mada travel

At home

2

W&

2

S&Wn-

tory

Person

f

(11) (12) (21 (2 (31) 32) ) (4 (50)

Total person-d

categories in order oestimation

(13) ) (22) 3) ( (33 0)

ays

Preschool 256 444 302 1,002 School pre-driving

11 1 1,604 27 13 243 153 2,052

School driving

25 1 269 21 45 42 39 442

University student

68 8 205 38 55 73 65 512

Full-time worker

4,007 426 31 23 1 505 371 5,364

Part-time worker

665 79 9 6 1 261 129 1,150

Non-worker 50 6 27 1,940 989 3,012 Each person type is associated with only a limited number of frequent DAPs that are modeled. A pattern that includes work and university tours is interpreted as “work-university” for workers while and as “university-work” for university students. The important feature of the proposed DAP model is a linkage across household members. While at the stage of statistical analysis and model estimation, any sort of cross-linkages can be explored, in the application stream of the choice models there must be a “non-cycling” chain of choices according to a predetermined order of person types. Relatively “independent” person types should be processed first, while the more “dependent” person categories should follow them and take the choices made by the previously modeled persons into account when making their own decisions. The following statistical observation and subsequent assumptions have been made to justify the sequencing of person types adopted for the model system recently developed for MORPC and ARC:

• In general, adults adjust their schedules to serve children and not vice versa. For example, child sick at home with a very high probability will require at least one of the adults to stay at home (for preschool children it was over 90% of cases for sickness and over 80% for other cases). Workers quite frequently take day-offs to take care on children. It is less usual when children do not go to school to take care on adults. Family vacations, though organized by adults, are also coordinated with school holidays when children are involved.

• Full-time workers and university students are characterized by the lowest percentage of joint daily activity patterns with children comparing to part-time workers and non-workers. Their schedules are less flexible than schedules of part-time workers and non-workers. In this sense



these person types are more individualistic than part-time workers and non-workers. Thus, it

r odate university

•

• Younger non-working homemakers are positioned after retired persons since the retired

way

and the corresponding rules of linkage are shown in a

schematic way in Figure 5.3.9 below.

makes sense to position university students and workers before part-time workers and non-workers.

• When we consider co-operation at the entire-day-level across household adults (having vacation together, going for major shopping jointly, etc) it is assumed that, all else being equal, it is easiefor part-time workers and non-workers adjust their schedules to accommstudents and full-time workers than vice versa.

University students are considered as of relatively higher priority comparing to workers since they normally have less flexibility in their mandatory activities and also university students living in family households with workers are frequently grown-up children.

household members may be physically limited in implementing household maintenance task. Inthis sense, the younger non-workers normally take a role of the household member responsible for maintenance tasks (this is relevant for the ARC model system only where non-workers where additionally subdivided into homemakers under 65 years and retired persons).

• If there are several household members of the same type they are ordered by age in such athat the youngest person is assigned a higher priority.

The resulting sequence of person categories

At home Non-mandatory At home Non-

mandatory

Children & adults Across adults

1. Preschoolchild

2.School childpre-driving

3. School childdriving

4. UniversityStudent

5. Full-timeworker

6. Part-timeworker

7. Non-worker

Figure 5.3.9. Intra-Household Linkage of Daily Activity-Travel Patterns



Most intrdistinctiimpact o ults (stemming from the child care function) from the cross-impact across adults them elv

1.

a-household impacts relate to the sharing of non-mandatory activities. It is important to make a on between in-home activities and out-of-home activities. It is also important to distinguish the f children on ad

s es. Thus, the following four basic intra-household impacts are analyzed:

Child care at home. If at least one of the preschool or school children stays at home, thleast one of the household adults might also stay

en at at home to take care on the child.

2. Escorting child for non-mandatory activity. It is assumed that if a non-mandatory pattern has been chosen for a child on a regular workday, it is most frequently associated with visiting the doctor, out-of-home family event, sports event, etc; thus it may require escorting by an adult f tory

3.

amily member. Thus, in order to take into account a probable time conflict with the mandaactivity, this adult should also have a non-mandatory DAP.

Sharing in-home non-mandatory activity by adults. If at least one of the household adult members stays at home (or is absent, travels out of town, has a vacation) there is a probability

h

for the other adult household members to join him/her. Thus, the at-home/absent utility for eacsubsequently modeled adult person category includes an indicator of the staying at home for thepreviously modeled adults.

4. Sharing out-of-home non-mandatory activity by workers and students. If at least one of the household adult members with mandatory commitment has chosen a non-mandatory DAP (doff for major shopping, vacation, family event) there is a probability for the other adult householdmembers to join him/her. Thus, the non-mandatory utility for each subsequently mo

ay-

deled adult person category, should include an indicator of non-mandatory DAP for the previously modeled

ey on the sharing of the ying-at-home and non-mandatory DAP by household members. Note that the total shown in the last

adults. Table 5.3.4 summarizes the statistics from the MORPC household travel survstacolumn is not equal to the numbers summed across all alone and shared cases in the row, because morethan two household members may share the same DAP. Table 5.3.4. Sharing At-Home and Non-Mandatory DAPs

Number of observed patterns (days) Shared with

Person category Alone

in the HH

Pre-school

School-predriv

School-driving

Univ-Student

Work-full

Work-part

Non-work

Total

At home: Preschool 42 66 33 3 7 58 27 79 221 Sch-predr 28 33 30 9 5 41 12 23 120 Sch-driving 13 3 9 4 2 9 3 8 35 Univ-stud 32 7 5 2 4 6 2 5 55 Work-full 166 58 41 9 6 42 20 53 328 Work-part 63 27 12 3 2 20 1 19 128 Nonwork 554 79 23 8 5 53 19 143 836 Non-mandatory daily pattern: Preschool 108 79 41 1 12 47 36 149 360 Sch-predr 55 41 51 13 6 47 15 50 181 Sch-driving 11 1 13 3 2 12 5 9 39 Univ-stud 41 12 6 2 2 6 3 9 71 Work-full 289 47 47 12 6 30 27 78 475 Work-part 160 36 15 5 3 27 2 34 259 Nonwork 984 149 50 9 9 78 34 337 1,586



From this table, the following observations can be made regarding the sharing of at-home DAP:

• University students and non-workers have the highest percentage (60-70%) of staying-at-home pattern alone.

• Full-time and part-time workers have approximately 50% of staying-at-home alone DAPs, while the other 50% of cases are shared with the other household members.

o are characterized by the lowest en -3 f staying-at-h t

er freq ing e alone” n ally i pre f ba ter w t a member of the hou or several household adults ta are dut rn.

Si an be made regarding the sharing of non-mandatory DAPs. The DAP type choice m l has been lie e M and ARC mo em rese tion for M d d d in ha 200 d [ sultall person types respectively. Linkages across different members of the household that reflect sharing of the same activiti or jo vel d to tre tron istic is i s thimportance -ho d d on f el h p s DA

Explicit Mode Jo av

Explicitly modelin tra po or b eo and cal s. F e acbehavioral standpoint it is toge di de hat to j avel in order to choices of mode, destination and TOD period. In practical terms it is important to properly model high occup hic ) m en ncti ho d co tionactivity participa er od es a qu hap n m ses, efle

• Chpercwith some ad

ildren, especially of preschool and scho l pre-driving agelone intage (20

ult householdorm

0%) o

mplies

ome a(most either a taking

, whileuently, worker or nbysities in tu

the mos

ho is no

of the caon-worker). “S

ses they stay at home tay memb

sence oking c

at homsehold

milar observations code estimated and app d in th ORPC del syst s. The ults of the model

stima ORPC an ARC are iscusse [Vovs et al, 4a] an PB Con , 2004] in detail for

es and/ int tra prove be ex mely s g stat ally. Th ndicate e of the entire usehol imensi or mod ing eac erson’ P.

ling of int Tr el

g joint vel is im rtant f oth th retical practi reason rom th tivity-important to link ther in vidual cisions t relate oint tr

avoid conflicting ancy ve le (HOV ovem ts as fu ons of usehol mposi and

tion rath than m e choic s it fre ently pens. I ost ca HOV r cts thein ehold tio tivi her ndividual mode choicetra-hous coordina n of ac ties rat than i . Attributing HOV to network le ice c ris e ho ework can l a s ver atio e H f th lanes introdu e of har e network improvements can be c ptured by accessibility indices carried up from the tour-lpattern level choices. Making a j ires si atio rder mbc ries to a ble uct still ng ost ntly ved Thfo t

Fully joi a er us mem trav the ly f e origin th ll d ons etur e rig

Joint ha the principle as f t t t ap or e rect g (outboun nbo epa . Ea jo r b ition join

owever, there may be joint half-tours that are not part of joint tours, when household member tog or e le

Drop-off driver/full-journey rider – a traveler who starts the half-tour with another household member ve er of on top e th ary estihas been d.

out passe – the corresponding h hold me alf-thouseho be d a rne but leaves him befohe/she has reached the primary tour destination

vel-of-serv haracte tics in th mode-c ice fram ead to erous o -estim n of thOV travel as a result o e HOV ction. S nsitivity HOV s e to th

a evel choices to activity-

oint t del operationravel mo al requ some mplific ns in o to reduce the nu er of atego managea constr while

ed: coveri the m freque obser cases. e

llowing definitions have been adop

• nt tour – tour wh e two or more ho ehold bers el toge r entire rom through a estinati and r n to th same o in.

• lf-tour – same or join our, bu plied f ach di ional led and i und) s rately ch fully int tou y defin consists of two t half-

tours. Hs travel ether f only on g.

• who lea s him/h at one the sec dary s s befor e prim tour d nation reache

• Get- ngerld mem

ouse mber who starts the hfull-jou

our with the /her r define s a drop-off driver/ y rider re

.



• Pick-up driver/full-journey rider – a traveler who starts the half-tour alone but is joined by other household members at one of the secondary destinations before the primary tour destinationbeen reached.

• Get-in passenger – the corresponding household member who joins the household member defined as a pick-up driver/full-journey rider at one of the secondary destinations and accompanies

has

him/her to the primary destination.

n

und in complex multi-destination tours.

•

When cjoint-toutravel-mwhen st An analregions models have been recently developed by PB Consult. Table 5.

lculat ber of tours with expansion factors specific to the household day travel.

sent

t travel category, roughly equal to all partially joint and mixed cases taken

Sec l New York reg mentation by five purposes (work, university, school, maintenance, disc iopur every frequent for three purposes – school, maintenance and discretionary. In contrast, tours to work and univ si to have a significant

are of one-directional joint travel stemming from the fact that school and other journeys (work,

r is taxi,

• Other partial case – traveling together on some intermediate part of either outbound or returleg without starting the half-tour or reaching the primary destination together. This is a relatively infrequent case fo

• Mixed case – a possible combination of any of the defined above categories where several household members are simultaneously involved. For example, several household members can have a joint tour while some other household members can join them on a partial basis (get-out or get-in).

Individual tour/half-tour – a tour/half-tour made without any full or partial joint travel with any other household members.

onsidering joint travel, it is important to distinguish between the person-participation and entire-r units. A fully joint tour of two household members is counted as one unit within the joint-odel framework. However, it contains two person-participation tours that are counted separately atistical analysis is made at the person level across both individual and joint tours.

ysis has been implemented with household travel behavior data collected in two metropolitan – the Mid-Ohio region and the New York region – two areas where tour-based travel demand (MORPC and NYMTC)

3.5 presents a distribution of tours by the categories defined above. The proportions have been ed based on the expanded numca

category and area. Only home-based motorized tours have been included, and only for weekFor non-home-based tours (at work, at school, at university) an insignificant percentage of joint household travel has been observed. This, however, should be attributed to an objective limitation of modeling only household joint travel at this stage. First, consider all-tour statistics. Joint travel represents a significant percentage of the total travel, closeto half of the Mid-Ohio tours, and more than one-third of the New York tours. Fully joint tours reprehe most important joint

together.

ond y, consider further details of the tour distribution by categories for each travel purpose. The ional model is based on seg

ret nary). The Mid-Ohio regional model employs additional segmentation of the maintenance pos into four sub-purposes (shopping, eating out, escorting, and other maintenance). Joint tours are

er ty are almost exclusively individual. Additionally, school tours are shownshmaintenance-escorting) can frequently be synchronized only for one leg. Thirdly, among the mode-specific tendencies it should be noted that drive alone is logically applied exclusively for individual tours. If one of the trips on the tour was implemented as HOV, the whole touconsidered as HOV. HOV logically proved to be the most frequent mode for joint tours, followed byand school bus. Transit in general proved to be mostly an individual travel mode. The vast majority of



HOV travel is undertaken by intra-household travel parties (75-80%) with the remaining 20-25% associated with inter-household travel. Transit and taxi samples in Mid-Ohio are very small since the total share of transit and taxi in the region is only about 1.0%. Table 5.3.5. Tour Distribution by Joint Category

Joint category Region, purpose, mode

Indivi-dual

Fully joint tour

Joint out-bound half-tour

Joint in-bound half-tour

Drop-off out-bound

Get-off out-bound

Pick-up in-bound

Get-in in-bound

Other, mixed

Total

All home-based tours 100% NY 62.3% 21.4% 3.7% 2.2% 2.2% 2.0% 2.0% 1.7% 2.6% 100% MO 53.8% 18.9% 3.9% 3.2% 5.9% 5.1% 4.0% 3.4% 1.9% 100% Work tours 100% NY 84.4% 3.0% 1.2% 0.8% 5.0% 1.1% 3.5% 0.9% 0.0% 100% MO 82.1% 2.2% 1.5% 1.6% 6.4% 1.0% 4.4% 0.7% 0.1% 100% University tours 100% NY 86.7% 4.1% 1.7% 1.3% 2.9% 1.5% 1.3% 0.4% 0.0% 100% MO 83.9% 5.8% 1.2% 2.1% 2.9% 1.2% 1.5% 1.4% 0.0% 100% School tours 100% NY 45.2% 21.1% 7.3% 3.7% 1.7% 7.0% 4.0% 3.2% 7.0% 100% MO 33.7% 18.7% 12.8% 8.4% 2.7% 10.1% 4.1% 5.7% 3.8% 100% Maintenance tours 100% NY 48.0% 35.4% 4.7% 3.9% 0.7% 1.5% 0.7% 1.8% 3.1% 100% MO/shop 59.7% 32.4% 0.6% 1.3% 2.0% 0.5% 1.1% 1.0% 1.5% 100% MO/eat 33.6% 51.3% 1.6% 3.2% 3.1% 1.6% 2.1% 1.7% 1.9% 100% MO/escort 23.0% 20.2% 22.4% 20.0% 1.3% 3.4% 1.5% 3.4% 4.8% 100% MO/other 52.6% 31.8% 3.3% 3.0% 2.6% 1.9% 1.6% 2.3% 1.0% 100% Discretionary tours NY 46.1% 41.1% 2.1% 1.3% 0.9% 2.1% 0.4% 2.2% 3.7% 100% MO 46.3% 32.6% 4.9% 4.0% 1.8% 3.4% 1.7% 3.8% 1.5% 100% Single occupancy vehicle (drive alone) NY 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100% MO 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100% High occupancy vehicle (shared ride) NY 20.2% 46.9% 7.5% 4.4% 4.7% 4.8% 1.5% 3.8% 6.2% 100% MO 23.6% 34.7% 9.7% 8.7% 6.0% 5.1% 4.7% 4.3% 3.2% 100% Transit NY 83.1% 9.9% 2.0% 0.9% 1.2% 0.6% 1.4% 0.6% 0.4% 100% MO 94.4% 5.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100% Commuter rail NY 84.0% 5.0% 1.8% 1.1% 4.7% 0.4% 2.5% 0.0% 0.6% 100% MO N/A Taxi NY 65.5% 23.9% 1.6% 1.2% 0.6% 0.6% 1.7% 2.3% 2.7% 100% MO 88.3% 11.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100% School bus NY 65.5% 20.8% 4.8% 2.8% 1.5% 0.6% 3.0% 0.4% 0.7% 100% MO 65.9% 24.5% 3.3% 4.4% 0.0% 0.0% 0.1% 0.1% 1.8% 100%



In general joint travel frequency patterns in the New York and Mid-Ohio regions are quite similar and lead en the regions is in the

n the Mid-Ohio region,

n areas

ed from the activity-participation point of view. This analysis is implemented based on the travel purpose and visiting location. The following principal categories of joint travel ar

to the same implications for modeling. The most important difference betweproportion of partially joint travel (drop-offs/pick-ups) that is significantly higher iand is especially frequent for work/school combinations. This probably reveals a general tendency that is more associated with less dense suburban areas (most of Mid-Ohio), than with high-density urba(most of New York). At the last stage joint travel is analyz

e distinguished:

• Joint travel generated by the shared activity. This category is almost exclusively bound to non-mandatory activities (shopping, eating out, other maintenance, and discretionary activities) as well as almost exclusively implies a fully joint tour structure. Cases where fully joint tours include

t tours an escorting function have been assigned to the escorting category. Cases where fully joininclude mandatory activity (work, university, school) have been assigned to the synchronized mandatory activity category.

• Joint travel to synchronized mandatory activities. In this case, joint travel occurs as a result of synchronization of individual activities and implies only a travel co-operation without sharing thactivity. Thus, the modeling technique for synchronized mandatory activity cannot employ a joint destination choice and time-of-day scheduling as in the case of shared activity, but

e

rather should link individually made choices in time and space. This category has a significant share of drop-

e of rs.

offs and pick-ups of school children made by workers. Additionally, a significant percentagschool children travel together to and from school generating fully joint tours and joint half-touNote that even going to the same school is not considered as shared activity because it has an individual character.

• Escorting that is a reported “altruistic” purpose of driving some other household member without

A mean rization of joint travel is necessary to properly model the corresponding tours and to link them with the various household members. The formal structure of joint trav (seffectivtour genexplana ction of one of the hou o

participation in the activity.

ingful activity-based catego

el ay fully joint tours versus partially joint tours) is not enough for an understanding and an e statistical explanation of the underlying travel-generation mechanism. For example, a fully joint erated by participation in the same activity requires a different modeling technique and set of

tory variables than do fully joint tours generated by a pure escorting funseh ld members.

Each of the three joint travel categories is modeled by a sequence of three choice models:

• Frequency choice, that returns a number (with the corresponding probability) of joint tours generated by a household,

• Travel party composition in terms of person categories participating in each tour (adults, children, etc),

• Person participation in each tour for each of the household members. Generation of joint travel is basically an entire-household function, thus the tour-frequency model comes rst and is applied at the household level. In order to link joint travel to the persons in the household,

ty composition model allows for narrowing down a subset of

fianother two models – travel party composition and person participation – are then applied. It has been found effective to decompose person assignment for joint travel into these two models, because the formulation of a single model that distributes household members by joint tours proved to be too combinatorial and complicated. A travel par



household members relevant for each joint travel category, thus, making the subsequent person participation model operational. For some joint travel categories the order of choice may be reversed. For example, the modeling of escorting tours starts with the choice of the participants (escorting and escorted persons). Frequency choice and travel party composition models generally fall quite readily into the standard discrete choice structure. Regarding the person participation model, two alternative ways to formulate the choice model have been found. The complexity of the person participation model stems from the combinatorial variety of households (especially relatively large households with, say, two workers and four children). Fully joint tours generated by the shared activity are the most frequent and important joint travel category. Synchronized mandatory activity follows in importance, then escorting. The MORPC model includes only the model for fully joint tours generated by shared activity. The proposed design for the ARC model and DRCOG (advanced stage 4) includes all three main types of joint travel. The basic structural features of the modeling sequence are shown in Figure 5.3.10 below.

Frequency of fully-joint tours generated by shared activity

1 tour 2 toursNo tours

Shop

ping

Shop

Eat/

Eat/m

ain

Eat/et

ioy

Eatin

g ou

t

Mai

nten

ance

nar

shop

Dis

cr

Shop

/ /eat

Shop

/mai

n

ain

/dis

c

disceat

in/m

op

Ma

Sh Mai

n/di

sc

Dis

c/di

sc

XTravel party composition for shared activity

Adults Children Adults+Children

By purpose

Person participation in shared activity

Yes No

By purpose &

X

nd person participation) the modeling

travel party

Figure 5.3.10. Modeling Fully Joint Tours with Shared Activity For each of the three models (frequency, party composition, aaspects are described in detail in [Vovsha et al, 2003b].



Alloca

The mo rs relate to

tion of Maintenance Activities to Household Members

del for individual tours for household maintenance activities allocated to the household membe two different decision-making units – household and person. Although these tours are nted individually, the basic needs served by mainimpleme tenance activities, relate to the entire

household. This model is subdivided into two successively applied sub-models, each of them formulated as a

•

discrete choice model:

Household tour frequency choice model for maintenance activities implemented individually,

Allocation • of maintenance tours generated by the frequency-choice model to household members

002 04. The technical advantage of the proposed approach is that it results in a

mparatively simple discrete choice structure that can be readily estimated using standard software like

ty-

• Sub-model for individual tours for household maintenance activities allocated

(i.e. choice of the person for each tour). The combination of these two models preserves the integrity of the model outcome at the entire-household level, while it also takes into account the individual availability of each household member at the allocation stage. It is preferable in our view to an unambiguous relation of maintenance activities to either household or person level. This modeling technique can be considered as alternative way to account for group decision-making mechanism addressed in research works of Gliebe & Koppelman, 2and Zhang et al, 2002, 20coALOGIT, and can be applied within the framework of a regional travel demand model. According to the placement of the model for individual tours generated by allocated maintenance activities, they are modeled for each household and person conditional upon the chosen daily activitravel patterns (which fully describe mandatory activities) and participation in joint household tours. The individual tour generation model for non-mandatory activity includes 3 choice stages modeled successively – see Figure 5.3.11 below:

to the household elates members; though these tours are implemented individually the basic need in this activity r

to the entire household.

• Sub-model for individual tours for person discretionary activities; it is assumed that these activities are generated and scheduled at the person level without significant interaction amhousehold members (recall that joint tours generated by shared discretionary activity of severhousehold members are modeled before in the model stream).

ong al

• Model for non-home-based sub-tours at work.

ctivities are generan u e Individu ormally have a low p n terms oactivitie ost crucial determinant. Wo least onpurposeand ent nce tasks.

Individual tours generated by allocated maintenance activities are modeled first for each person conditional upon the chosen daily pattern and participation in joint household tours. Since these a

ated by the entire household and then allocated to particular members, it is important to follow nd rlying intra-household allocation process.

al tours for personal discretionary activities are modeled the next because they ner riority in scheduling. Intra-household linkage is less important at this stage. Person availability i

f time window left after scheduling the mandatory activities, joint activities, and allocated s becomes the m

rk-based sub-tours are modeled the last. They are relevant only for those persons who implement ate work tour. These underlying activities are mostly individual (business-related and eating-out s), but may include some household maintenance functions as well that are linked to the person ire-household maintena



Individual mandatory and joint non-mandatory tours generated by

XX

the HH and each person at the previous stages

Household Person

0-2escort

0-2shop

0-3main

Individual MandatoryTour Frequency

Allocation of MandatoryTours

Individual discretionarytour frequency

Work-based sub-tourfrequency

Each touris

allocated

X0-2discr

0-2eating

XX0-1eating

0-2work

0-1main

Work tour

y l by themselves. However, presence of preschool children as well as their chosen

aily activity patterns (for example, going to kindergarten instead of staying at home because of r the other household members. Additionally

ersons who have chosen a stay-at home daily pattern are also excluded since they do not travel. A detailed description of the model for and estimation results can be found in [Vovsha et al, 2004b].

Ti -Day oice

Modeling dimen f t mplex components of travel demand models. It is usual greatly plif peak/off-peak facto s t at do not allow congestion-relief policy measures. stic over-congested situations wh ain purposes f he n gene ion o to fully include timing dimension into the

Figure 5.3.11 Individual Non-Mandatory Model Structure

Preschool children are not considered in the individual tour model as potential tour makers since thenormally do not travedsickness) is included as an important explanatory variable fop

allocated maintenance activities, the relevant statistical data

m f Ch (Tour Scheduling) e-o

sion o ravel timing is one of the most coly sim ied in the conventional modeling frameworks by means of constant

r h for any sensitivity to network level-of-service characteristics or Tracking these constant factors to future scenarios frequently leads to unreali

ereas in practice a sort of peak spreading is observed. One of the mo t ew rat f activity and tour-based models issystem of modeled choices along with a number of tours, destinations and modes, thus ensuring model sensitivity to the corresponding network or policy attributes. There is a rich research literature in both discrete choice trip departure time models and in activity scheduling models in general. However, there are only a few reported applications in the framework of

gional travel demand models. It is possible to distinguish between several main directions:

• Aggregate “peak fa

re

ctor” or “peak spreading” models that mostly operate in the framework of conventional 4-step trip-based regional model systems. The paper of Purvis, 1999 gives an



example while a systematic survey of the state-of-the practice can be found in the report prepared by Cambridge Systematics, 1999 for the US Department of Transportation.

• Individual activity scheduling models that consider time allocation, activity sequencing and scheduling in the entire-day framework. Most recent examples include, but are not limited to AMOS [Pendyala et al, 1998], ALBATROSS [Arentze & Timmermans, 2000] research works of Doherty et al, 2002, and Miller & Roorda, 2003.

• Departure time and duration models for particular types of activities or particular fragments of the daily activity agenda. Examples include a research of Pendyala, et al, 2002 on timing and

for social / recreational trips, and Bhat et al, 2002 on departure time choice for shopping trips.

The first direction is mostly limited to either aggregate “peak factors” or time-of-day choice models that perate with crude 3-4-hour intervals. The framework of conventional four-step models does not allow

-

ed

ity d

he practical perspective are their mplexity in application and presence of empirical components (postulated rules).

works in this direction provides important insights into the individual and household factors that affect timing and duration decisions. Focus oduration . However, direct use of the dura nestim tisystem, as well as the problem of consolidation of numerous activities in the entire-day schedule. The proapproac framework of a regional travel demand model. To position this approach

rsus the existing models, the following basic features and requirements should be mentioned:

sed or plan to use in several U.S. metropolitan areas (New York,

, Columbus, Atlanta, Houston, Sacramento and others).

n of aggregate TOD models, the model should be included into the ium framework and should be linked to the related destination, mode, and

d direction of daily activity schedule models, the approach should allow for tire-day schedule that spans all tours and activity episodes of the individual

in the course of a day; this in turn requires a formulation of the model at the enhanced level of

duration of maintenance activities, Steed at al, 2000 on departure time choice

ofor incorporation of variety of behavioral aspects pertinent to the activity timing and duration choices. It also does not allow for explicit relation of different timing and duration choices made by individual in the course of a day. However, one important positive feature of this approach is that it explicitly links timerelated travel choices to the travel conditions in the network in an attempt to achieve global demand-supply equilibrium. The second and third directions relate to a growing body of research on activity scheduling that is bason a continuous representation of time and frequently employs duration “hazard” models. Comprehensivescheduling models (the second direction) frequently include rule-based algorithms instead or in additionto duration models and discrete choice structures in order to reconcile various timing and duration decisions in a realistic entire-day schedule. The most important challenge of the comprehensive activscheduling models is the integrity of the daily schedule that requires a full consistency of all modeletime-related choices. The relative drawbacks of these models from tco The third direction is focused on particular types of activities. The

n a particular type of activity and a limited number of episodes allow for a closed formulation of models and comprehensive statistical analysis and estimation

tio models in regional travel demand models has been hampered by seeming complexity of the a on of these models, incompatibility with the rest of the models (mostly discrete choice) in the

posed TOD modeling approach represents an attempt to combine advantages of the existing hes in an operational

ve

• The model should be used within the tour-based, discrete choice micro-simulation framework, aframework that we have uPortland, San Francisco

• Similar to the first directiooverall network equilibrroute choices.

• Similar to the seconmodeling a realistic en

temporal resolution.



• Similar to the third direction of departure time and duration models, the approach should allow ration of various personal and household attributes that affect timing and duration of

It s l een a tour-based and a trip-based TOD

odel in that the tour-based model must simultaneously predict when a tour leaves home and when it e

sed only four or five broad TOD periods across the day (e.g. AM peak, PM peak, midday and off peak), riods ntage

ns is del

the MORPC and ARC model system as well as in the proposed design for DRCOG, the tour-scheduling

he choice alternatives are formulated as tour departure from home-arrival at home hour combinations

including intermediate stops.

, leaving home in period s

for incorpotours/activities.

hou d also be mentioned that there is a major difference betwmarrives back home The few examples of tour-based TOD models put into practice to date, however, havuwith a discrete choice from among 10-15 feasible departure/arrival combinations of those broad pe[Bradley et al, 1998, 2001]. With such coarse time of day resolution, it is not possible to take advaof the continuous nature of choice dimensions such as departure time and tour duration. From this side, the advantage of duration models that specifically address duration-related decisioappealing [Bhat, 2001]. Decisions regarding the duration of an activity can be best described by a mothat naturally incorporates the conditioning of the activity-termination probability at each time spell on the duration of the activity undertaken so far. Inmodel is placed after the destination choice and before mode choice. Thus, the destination of the tour and all related destination and origin-destination attributes are assumed known and can be used as variables in the model estimation. Contrary to that, mode is not known and only the composite mode-choice log-sum can be used as a variable. T( hg, ), while mode choice log-sums and bias constants are related to multi-hour departure-arrival periods ( ts, ). Tour duration is calculated as the difference between the arrival and departure hours

( gh − ), and incorporates both the activity duration and travel time to and from the main tour activity

The tour TOD choice utility has the following general form:

⎟⎠

⎞⎜⎝

⎛+++= ∑−

mstmghhggh VDVVV lnµ ,

here: w

hg VV , = departure and arrival time specific components,

ghD − = duration-specific components,

m = entire-tour modes (SOV, HOV, transit, bi-modal, non-motorized),

stmV = mode utility for the tour by mode m

(containing hour h) and returning home in period t (containing g) µ = mode choice logsum coefficient. Departure and arrival hour-specific components are estimated using generic “shift-type” variables (household, person, and zonal characteristics) with a limited set of TOD period-specific constants. Just aduration “shift” variables are multiplied by the duration of the alternative, d

s eparture “shift” variables are

ultiplied by the departure hour: m



∑ ××+=k

kkgg xgV βα ,

where:

gα = departure time constant for the TOD period.

A full set of departure time constants is not necessary; for example, all periods within a longer, composite eriod can be constrained to have the same constant. The variables examined in the departure and

xhg )(βα .

mber

tion has an index with dimensionality h

The prohome a“continuchoice smodel (includinexpress For mod household survey data, the following practical rules were use o

• “17”

•

• arrival an alternative. This gives 19 * 20 / 2 = 190

To spec le, departure/arrival constants were only applied for seven T

• • • • •

•

parrival components are mostly Boolean dummies (person types, presence of children and other householdcharacteristics, destination in CBD, etc). In a similar way, the duration-specific component is estimated in the following form:

∑ ×−×+= −−k

kkghgh

The coefficients are interpreted in terms of longer/shorter durations. Note that the index of the durationcomponent is (h-g) rather than (g×h), making the estimation procedure much simpler since the nuof duration alternatives is much less than the number of departure/arrival combinations. It should be noted that none of the estimated components of the utility func

D

g× .

posed model is essentially a discrete choice construct that operates with tour departure-from-nd arrival-back-home time combinations as alternatives. The proposed utility structure based on ous shift” variables represents an analytical hybrid that combines the advantages of a discrete tructure (flexible in specification and easy to estimate and apply) with advantages of a duration parsimonious structure with a few parameters that support any level of temporal resolution g continuous time). The hybrid model currently has a temporal resolution of 1 hour that is ed in 190 hour-by-hour departure-arrival time alternatives.

el estimation using the MORPC and ARCd t set the alternative departure-arrival time combinations:

Each reported/modeled departure/arrival time is rounded to the nearest hour. So, the hourincludes all times from 16:30 (4:30 pm) to 17:29 (5:29 pm).

Any times before 5 (5 am) were shifted to 5, and any times after 23 (11 pm) were shifted to 23.This was relatively few cases, and limits the number of hours in the model to 19.

Every possible combination of the 19 departure hours with the 19 arrival hours where the hour is the same or later than the departure hour ischoice alternatives.

ify the model as parsimoniously as possibOD periods (with minor adjustments discussed below in the section on the model estimation):

5 to 6 (early morning) 7 to 9 (AM peak) 10 to 12 (early midday) 13 to 15 (late midday) 16 to 18 (PM peak)

• 19 to 21 (evening) 22 to 23 (late night)



The netbroader

• • •

The mo The eassumedescribeactivitie ional upon scheduling the mandatory activities, they

hedule joint non-mandatory activities - maintenance and discretionary – of which maintenance

ities, each household member schedules individual activities within

e residual time window remaining after making any mandatory and joint tours. When atours arschedul r, and also forcing

e second tour to be scheduled after the first tour (even if there is an available residual window before

an be unambiguously ordered by scheduling priority. The residual time window and set of available TOD alternatives are defined for

cessed tours. A detailed Vovsha & Bradley, 2004].

ivity

Loc ndiscrimiimportainde npivot po Distant duces

ore stops. This gives rise to a modeling structure where regular workplace (school, university) locations

a

tors for

ny

work simulations to obtain travel time and cost skims are currently implemented for four even periods:

• AM peak Midday (including early and late midday) PM peak Night (including early morning, evening, and late night)

de-choice log-sums were used for all relevant combinations of the four time periods above.

pr determined hierarchy of tours by travel purpose and activity setting (individual/joint) was d in the scheduling procedure. This hierarchy is based on the general modeling principles d above. According to these principles, people first make decisions regarding their mandatory s (work / university / school). Then, condit

sc(shopping, escorting other persons, and various other household maintenance activities) is generally considered of higher priority comparing to discretionary activities (leisure and eating-out). Finally, havingscheduled mandatory and joint activth

person undertakes several activities (tours) of the same priority in the course of the day, those e prioritized in chronological order, i.e. the earlier tour is scheduled first, while the later tour is ed next conditional upon the departure/arrival time combination of the first tou

ththe first tour). By using the rules described above, all tours of each surveyed individual c

each subsequent tour conditional upon scheduling of the previously prodescription of the model estimation and application results can be found in [

Long-Term Choices for Location of Mandatory Act

atio of basic mandatory activities relative to the residential place is arguably the most important nator of travel behavior. Residential and workplace (school, university) serve for commuters as nt pivot points in space. Other destinations for either stops during the commuting journey or

pe dent home-based tours are mostly located within the ellipsoidal spatial envelope having these ints as focuses.

commuting usually tends to eliminate other independent home-based tours, however, promare modeled first and then all tour-generation and spatial distribution models include a commuting distance (and possibly other location attributes) as an important explanatory variable that limits both number of other tours and a set of potential destinations. Mode-choice considerations for journeys to regular mandatory activities are usually important fachousehold car-ownership decisions. While in conventional travel models car ownership is usually modeledfirst and serve as an input for subsequent trip generation, distribution and modal-split stages, in macases households own additional cars based on the workplace (university, school) accessibility and long-term intra-household arrangements stemming from that. Thus, placement of the car-ownership model after the long-term activity locations is justified – see Figure 5.3.2 above.



Additionally (this is more a consideration for the advanced stage 4 of the model system development), mod n tional link version model awith -location e a joint res nting for all household members. From the ousehold perspective residential place should ideally be a central point for long-term activities unless

. For

oices

hich

tinction should be

kept. In particular, transit accessibility is more important for school choice and for both workplace

olds or n

ve

ces in

ld

a

om the production side. In the fully disaggregate framework it means that a binary model (stay

eli g workplaces (school, university) for several household members allows for (possible) addiage at the household level for all location-related decisions including residential choice. At the current

of model system we will stay at the independent forecasting of population for which we than ll activity-location choices. Advanced integrated land-use & transportation models usually start

so called basic employment and then consider residential choice conditional upon workplace . In reality, the truth is probably somewhere between these approaches and there should bidential-workplace (university, school) choice accou

hhousing price dictates moving to outskirts. Some partial considerations from this approach can be of use even in the current limited frameworkexample, for multiple-worker households it is highly improbable that both workers will have a long commuting. In this situation a household usually considers changing the residential place. If we can capture this effect statistically, it can narrow dawn a set of destination zones in both model calibration and application. Long-term choices for mandatory activity locations are similar to ordinary (short-term) destination chin that they have the same two basic components – zonal size-attractiveness variables and travel impedances – with the same MNL modeling technique that suggests itself. However, the way in wthese components are calculated as well as a set of additional considerations and household-level restrictions make these models quite different from the ordinary destination choice models. The followingspecial features of the long-term choice should be mentioned:

• Long-term choices should be based on a more simple and general impedance measure thanshort-term destination choices; however, transit vs. highway accessibility dis

and school choice of low-income households. • For workplaces it is important to stratify both workers (individuals) and jobs (zones) by income

(earnings). Since the population-forecasting model deals with household income (that is not equal to individual earning, for example a high-income household can have a low-earning worker) it is necessary to either forecast workers distribution by earning within the househredistribute jobs in terms of household incomes rather than earnings. The last approach has beesuccessfully applied in New York BPM.

• It makes sense to explicitly stratify both workers (individuals) and jobs (zones) by full-time vs. part-time status as well (if it is possible from the job-attraction side). Another option is to sathis distinction for the tour-generation set of models where resulting tour frequencies will incorporate attendance factor implicitly. However, zonal-specific features of jobs will be lost in the last case. The New York BPM experience has shown that there are significant differenjob-attendance factor by areas. For example, average journey-attraction rate per job in Manhattan is close to 0.91 while for the rest of the metropolitan area it is 0.70 reflecting a part-time schedule for many local jobs. This is probably a general tendency for CBD and basic employment to exhibit higher rates of attendance.

• It is worth trying to stratify workers within multiple-worker households by primary (the househohead) and secondary, where the secondary workers will probably have a stronger dispersioncoefficient for the impedance measure.

• Choice of university is frequently associated with temporarily changing the residential place forstudent (rent apartment or dormitory). Trips to university is probably a single travel purpose where attractions are better predicted that productions. Thus, it made sense to relax constraints fr



in the household vs. move to the university) should be applied for students in conjunction with the long-term choice.

• School choice, especially for lower grades strictly follow school districts. If we can producedistribution of children by age (grade) and school location and enrollment by grade are available, then the school choice becomes almost unambiguous with district-boundary dummy as the strongest variable in impedance function. Ordinary

mode choice log-sum is not applicable for

•

er stronger determinant here. Thus, if it had been a joint residential-workplace

• e

App tof the slogit for

• (Upper level) - binary choice of regular location vs. “other” location, • (Lower level for the “other” location nest) – MNL choice across zones for “other” location (may

include the same zone as for the regular location) with a special set of size variables calibrated for only the tours that have a destination different from the regular location.

Statistical analysis will show if these levels should be linked at all, maybe two separate MNL models applied in sequence will be enough. Additionally, it is believed that a tag “regular vs. other” location can be useful for other models (like stop-frequency or mode choice) as well.

Conceptual Lines for Further Model System Integration

In the most general way these conceptual directions can be classified as the following “lines of integrity” in modeling various travel-related multidimensional choices:

• “Intra-person integrity

school because of the school bus presence and mode availability limitations. Simple distance in addition to district-boundary dummy worked reasonably well in the New York BPM. It looks like an attractive idea to include activity pattern type log-sums as components for long-term choices of mandatory activities (i.e. if one chooses long-distance commuting, all else being equal, it can limit variety of other activities). However, a density of activities in the residential place can be anothlocation choice, the activity-pattern log-sums would definitely have been relevant. For pure workplace choice with fixed residential location, a simpler density should be also checked statistically. It is tempting to include either occupation type or educational level as additional dimensions for segmentation of workplace choice. However, taking into account difficulty in forecasting thesvariables and especially at the zonal-attraction side, it can be tried only at the advanced stage 4 of the model system development.

lica ion of a long-term choice model for workplace, school and university principally changes the form ubsequent tour destination-choice model. The tour destination-choice model will have a nested m with the following levels:

” of each modeled individual daily activity and travel pattern in a sense that all modeled activity episodes, their durations, locations, and travel tours associated with visiting out-of-home activities are consistent and feasible within the person time-space constraints.

• “Inter-person / intra-household integrity” that means that daily patterns of different household members are properly coordinated in view of participation in joint activities, joint travel arrangements as well as intra-household mechanism for allocation of maintenance activities, allocation of cars to the household members, etc.

Intra-person integrity is associated with a proper conditioning in sequence of choices related to each individual from the top-level choice related to the daily activity pattern type to the lower-level choice related to details of each activity episode. Intra-person integrity was in the core of the original concept of the daily activity pattern choice model [Bowman & Ben-Akiva, 1999; Bowman & Ben-Akiva, 2001; Bhat & Singh, 2000]. The major breakthrough that made this approach operational was the integrative formulation of the daily pattern in terms of a number and structure of travel tours rather than elemental



episodes that provides the necessary input to the subsobserved individual daily activity patterns and structur

equent set of travel models. The number of al complexity of the choice model in combination

of the

with a huge number of possible activity location alternatives make it impossible to model all dimensions inone choice structure. Thus, various hierarchical structures were proposed that resulted in a cascade conditional choice models. This inevitable decomposition leads to two different structural lines withinIntra-person integrity framework:

• “Downward Intra-person integrity” that means that all lower-level decisions in the choice hierarchy should be properly conditional upon the upper-level decisions and take into account gradually narrowed scope of lower-level choice alternatives as the upper-level c

a hoices progress.

• “Upward Intra-person integrity” that means that when modeling upper-level choices the composite measure of quality of the lower-level choices associated with each upper-level alternative should be properly taken into account

Downward intra-person integrity is not an automatic property of hierarchical cascades of choice moespecially if different activity dimensions such number of tours/activiti

dels, es, their location, and timing are

nsidered. For example, first activity-based models for Portland METRO, SFCTA, and NYMTC had es

.

d

erstanding the interrelationship between activity generation and scheduling stages nd their positioning in the model system hierarchy. Similar relationships should be further explored

between such dimensions as activity locations/durations and tour configuration in terms of a distribution of activity episodes by tours. Also possible substitution between in-home and out-of-home (travel) activities can be considered as a part of the downward Intra-person integrity issue. Upward intra-person integrity

coindependent-by-tours mode, destination, and TOD choice models that could produce conflicting choicfor different tours made by the same person. Downward Intra-person integrity is ensured by a proper sequencing of models and tracking all important variables from choice to choice that accurately describethe feasible scope left for each subsequent choice and prevent conflicting choices for the same individualIt has recently been recognized that time-use approach provides an operational framework for downwarIntra-person integrity because time serves as an ultimate and constrained resource for any type of activity. From this point of view, it proved to be more convenient to generate tours/activities and schedule them according to a certain hierarchy using residual time windows left after scheduling previously generated tours as variables explaining generation of the subsequent tours. Further research is needed to better unda

is important to prevent illogically bad choices made at the upper levels of the choice hierarchy that may result in impasse at the lower level (for example, if a worker who has three non-work tours in addition to the work tour has been assigned a work schedule from 7:00AM to 22:00 PM) as well as it is crucial for the model system sensitivity to travel environment from the upper-level activity generation choices. Conventional fractional-probability models use the log-sum (expected maximum utility over the lower-level choices) technique to “inform” the upper-level choices about what can happen down the hierarchy. This technique can be used in the micro-simulation framework as well, however it is extremely intensive computationally when it comes to calculation of tour mode choice log-sums for destination choice (takes more than 60% of running time of the model system) and is not realistic at all when full destination choice log-sums (across all destinations and TOD periods) are considered as variables for daily activity pattern model. One possible solution that is currently explored is to exploit the overa e generated lower-level utcomes from the previous iteration as variables in the upper-level choices at the next iteration. This pproach can be interpreted as “learning process”. Time-use framework also can be affectively used in

g-

next tal

continuous time allocation model (with travel budget as input variable) can be pplied first and then daily pattern type and the subsequent chain of choices can be made conditional

ll iterative framework of the model application and usoathis iterative procedure. Instead of feeding-back computationally intensive but actually quite abstract losums contracted over multiple choice dimensions a simple variable representing total travel time spent by individual to realize the activity pattern in time and space, can be fed-back and considered at theiteration for a choice of the new daily pattern. To make the upper-level choice sensitive to the toexpected travel time aa



upon the expected time allocation. With this actually very simple technically approach, the whole mochain will be sensitive to network improvements since these improvements are finally expressed in time savings. Inter-person intra-household integrity

del

principle includes numerous ways to incorporate intra-household interactions in a travel demand model, either explicitly or implicitly.

ujii the

et

• ither 004;

odels [Vovsha et al, 2003].

ers

•

• Using household composition variables (frequently presence of children of particular age categories) as explanatory variables in trip/tour generation or DAP models for workers and other adults. This approach can be classified as implicit.

• Explicit joint or at least coordinated modeling of daily activity pattern types (or related activity-travel characteristics) for several household members. Most frequently, time allocation units are used for modeling and the Structural Equation System is employed [Golob & McNally, 1997; Fet al, 1999; Meka eat al, 2002; Simma & Axhausen, 2001]. The proposed approach, used inMORPC and ARC system, however, is based on a linked set of discrete choice models [Vovshaal, 2004a]

Explicit modeling of joint activity and travel. This component has been modeled in terms of eepisode generation or time allocation between individual and joint activities [Ettema et al, 2Gliebe & Koppelman, 2002; Scott & Kanaroglou, 2002]. Explicit modeling of joint tours has been incorporated into the MORPC and ARC regional travel demand m

• Explicit modeling of within-household allocation of maintenance activities to household memb[Borgers et al, 2002; Srinivasan & Bhat, 2004]. The corresponding component has also been included and successfully tried in the MORPC modeling system [Vovsha et al, 2004b].

Explicit allocation of cars to household members that accounts for actual availability of a car for aparticular person’s travel tour [Wen & Koppelman, 1999; 2000]. This model component is reserved for future model development.



5.4. Micro-Simulation Technique Another related modeling tendency reflects the technical possibility to treat explicitly a full list of regional ouseholds and persons rather than combine them by zone and socio-economic strata. The current

les and arrays with tens of m s and hundreds of attributes. Thi ay for fully-disaggregate modeling approach w pa ial resolution in order to avoid numerous aggregation

hgeneration of hardware and software allows for effective handling data fi

illions record s opens a with a much higher travel segmentation and s t

biases and internal inconsistencies pertinent to the conventional models. Most of the choice models described ab n a micro-sim

M tional-Probab

to use tour-based modeling techni thout using micro-mulation. These meaningful components were first developed without this feature. However, it proved

on context. However, in the application stage of a

micro-simulation model, a Monte-Carlo approach is applied to simulate discrete choices at the individual mand dimensions to

produce shares of some travel segment. In most applications, these individual realizations from micro-

atistics, although the goal of some transportation rese ee Figu

ove would be feasible only i ulation application.

icro-Simulation vs. Frac ility Approach

It is possible si

que and activity schedule approach wi

to be extremely beneficial to apply these advanced concepts in combination with micro-simulation because it makes them practical in terms of required dimensionality. Technically, micro-simulation requires a core probabilistic model to be developed and calibrated. In general, there is not much difference in the statistical calibration of a standard disaggregate model fromthat of the model to be applied in the micro-simulati

level, rather than rely on the aggregation of fractional probabilities along travel de

simulation are aggregated to traffic analysis zones – by mode, vehicle type and time period – prior to network loading to obtain necessary traffic and transit st

arch, and TRANSMIMS for example, is the ability to assign discrete trips on a standard network - sre 5.4.1 below.

Core Probabilistic Model

Conventional Micro-Simulation

Monte-CarloMulti-DimensionalArray of Fractional

ProbabilitiesRealizations for

Individual Choices

Aggregation Along Travel Demand Dimensions

Network Facility Loadings

Figure 5.4.1. The Basic Concept of Micro-Simulation

In this mmodels. s of journassignm een implemented using a standard zonal user equilibrium ssignment method.

anner, micro-simulation can be readily combined with the conventional fractional-probability For example, in the New York BPM framework, micro-simulation has been applied for the stageey generation, mode split, destination choice and time of day choice. However, the final ent (route choice) stage has b

a



It should be stressed here that the application of the Monte-Carlo technique on the top of the coreprobabilistic model itself does not automatically improve either the behavioral structure or the acthe transportation model. If the core probabilistic model can be applied simultaneously across all traveldemand dimensions, then a Monte-Carlo game on the top of it will simply follow the basic travel statisalready available from the core model.

curacy of

tics

icro-simulation does, however, have three major advantages over either conventional aggregate or Mdisaggregate travel models that lack this method – see also Vovsha et al, 2002; Petersen et al, 2002 for a more detailed analysis of various aspects of micro-simulation based on the NYMTC model:

• First, micro-simulation offers a substantial savings in the calculation and storage of multi-dimensional fractional-probability arrays during model application. This allows for higher marketsegmentation that otherwise is extremely limited in the conventional modeling framework. In particular, a number of household and person attributes can be extended virtually without limitation as far as the corresponding variables can be supported and forecasted for model calibration and application. Additionally, a finer spatial resolution can be allowed for network level-of-service and land-use variables. As a result of easy and compact storage of the micro-simulation results, it is always possible to output a wide variety of reports across any travel dimensions or to single out any particular segment, for example, modal split for a specified person type only. In many cases with conventional or standard disaggregate models this would require re-running the model with changed reporting options.

• Second, micro-simulation allows for the explicit formulation of various chained decisions and time-space constraints on individual travel behavior. This behavioral realism has been recogand widely acknowledged as the primary theoretical advantage of micro-simulation. However,experience with calibration and application of the micro-simulation models has shown that thereis a certain “price” that must be paid before this benefit can be achieved. This includes an appropriate preparation of the core probabilistic model, including multiple conditioning atruncation of probabilities in order to incorporate additional linkages and constraints. In order to truly take full ad

nized

nd

vantage of micro-simulation, the core probabilistic model must be restructured to take chained travel behavior into account as allowed for by the micro-simulation approach. While

will require more effort due to the appropriate complication of the core probabilistic model.

g variability of

the final results will be more realistic than unlinked decision-making, the estimation and calibration stages

• The third potential advantage of micro-simulation relates to the explicit modelintravel demand, rather than average values. The last property can be exploited as a significant a in certain circumstances, whereas it can also be viewed as a problem in some other cases. While the variability of micro-simulation can be explored s al point of view, the complexity of the core probabilistic model makes certain aspects of this analysis still partially empirical. Based on this analysis, rules can be

of the m s. The ultimate use of the model will dictate the desired number of r produce a distribution of forecast volumes within the neces ry lso Castiglione et al, 2003 for systematic analysis of micro-simul

Addit a e of the whole modeling system to the t ensure convergence being applied in com n rithms have been developed based on ave i iterations. In the micro-simulation frame or nked to discrete individual records. It shou e combination with re-simulation of a limited sub-set of househ n. Empirical analysis of this strategy is still

dvantage of micro-simulation

tatistically from the theoretic

established for application of micro-simulation models depending on the dimensionality odel and planning purpose

uns w thi different seeds in order tosa degree of accuracy – see aation variability for the SFCTA model.

ion l pro embl atic feature of micro-simulation relates to the convergenc ne work equilibrium. Conventional fractional-probability models

erative algobi ation with networks assignment. The effective itrag ng travel demand matrices obtained at successive

d because matrices are liw k this technique cannot be applield b replaced with averaging level-of-service skims in

olds diminishing from iteration to iteratio



required as w a e following Table 5.4.1 summarizes t icro-simulation.

Table 5.4.1. Relative advantages and disadvantages of micro-simulation

ell s some new theoretical background should be built here. Thhe relative advantages and corresponding disadvantages of m

Advantage Problem Savings in storage and calculation allowing for high segmentation

Chained decisions and time-space constraints

Conditioning and truncation of probabilities

Explicitly modeling variability Instability of the results Convergence to network equilibrium

It should be stressed that while micro-simulation still has some unresolved issues, it is generally considered as the most advanced travel demand modeling “technology” available for the development of the next generation of transportation demand models, including of course TRANSIMS. In complex metropolitan regions micro-simulation constitutes the only practical way to handle all the related multi-dimensional calculations.

Synthesized Population – Controlled and Uncontrolled Attributes

Application of the micro-simulation model requires a full list of households and persons in each zone to be created. This is done in two meaningful procedures:

1. Forecasting household and person distribution by a limited set of basic attributes according to planning targets for each period (controlled segmentation in forecasting).

2. Random sampling of identical households from the PUMS for each category in order to represent other attributes (synthesizing population by uncontrolled attributes).

he first procedure is identical to the corresponding procedure of the conventional travel demand model. lso the variables required are more or less the same as for the conventional model. The first procedure

TApasses through three basic stages:

• Forecast zonal number of households and average values for all attributes included into the controlled segmentation (household size, income, number of workers etc). This is usually prepared as external input to the transportation model as a result of land-use or demographic model with a lot of manual adjustments. In many cases average zonal household attributes aknown only for the base year and then either they are assumed stable over the target ye

re ars or

o-dimensional distributions

adjusted to include regional demographic trends,

• Forecast the corresponding one or tw of households in each zone (for 4, 5+). This is prepared either as a part of the land-use and

r can be modeled by means of typical zonal distribution curves that map percentage of each household group as a function of the zonal household average.

example by household size 1, 2, 3, demographic forecast o

• Forecast joint multi-dimensional distribution of households in each zone by means of the iteratproportional fitting algorithm using the one and two-dimensional distri

ive butions as marginal sub-

er of households in each zone and multi-dimensional category in order to

totals and starting with a typical (base year) seed multi-dimensional distribution of households. Round up a numbcreate a list of households.



The second procedure is unique for the micro-simulation framework. For each modeled zone and ld category a random sample househo of the same number of households is implemented of the

hous oeh lds with identical controlled attributes from the PUMA that contains the modeled zone. All theolled) household and person attributes from PUMS households are attached to the synthetic lds. Thus the synthetic population on one

(uncontrouseho hand, will fit the controlled zonal targets, and, on the

old and person vyield acc aggregation bias

hother hand inherit the detailed structural features observed in the PUMS. It is important to use uncontrolled attributes to account for internal variability across househ

ariables that included into the choice model utilities. Even if the proposed procedure does not urate results it is much better than the that occurs if these attributes are

comp tevaria from thetravel fre ndently and usua rwhile ch drive alone. Combining them all in one category obscures these diffe c e choi mositive f the households with real attributes solves

hat the random pick of households for presenting uncontroobserved bjectively missing component that can be pointed out h reappl )rocedu mparable in most cases with the aggregation-over-attributes bias that is

The p below. The foll

le ly excluded. For example, though number of children in the household is usually a controlled ble a further subdivision by age categories (of each of the children) can be problematic. However,

travel demand perspective there is a significant difference across age categories with respect to quency, modes and destinations. Small children of age under 6 do not travel indepe

lly equire drop-offs and pick-ups from the parents. Children of age 7-16 travel alone by transit, ildren of age 17 and older can

ren es and also makes it difficult to establish reasonable mode availability rules. As a result modce odel for this combines segment usually proves to be statistically insignificant with illogical

signs for time and cost coefficients. Random draw opthis problem and make up for missing values in the most reasonable and unbiased way. Experience of the SFCTA, Portland METRO, MORPC, and ARC models where the proposed household-synthesizing procedure has been applied shows t

lled attributes at the level of zones yields finally a reasonable distribution that closely follows proportions of PUMS for each PUMA. The o

e is if the PUMA proportions for uncontrolled attributes are suitable for each small TAZ or (if ied geographic cell. Of course, some fine cell-specific differences can be overlooked in this

re; however, this is incopintroduced otherwise.

po ulation synthesis procedure developed for the MORPC model system is shown in the Figure 5.4.2

owing input parameters should be specified for each zone for each target year:

Total population Total number of households

• •

• The out

• Total labor force Average household income

put is generated as a list of households in each zone with the following characteristics:

Household income group: •

•

o Number of full-time university/college students

o Low – less than $30,000 o Medium $30,000-$74,999 o High equal of higher than $75,000

Adult household members (18 years and older) o Number of full-time workers (40 or more hours a week) o Number of part-time workers (leas than 40 hours a week)

o Number of non-working and non-studying adults • Household children (under 18):

o Number of preschool children (under 6)



o Number of school pre-driving-age children (6-15 years old) o Number of school driving-age children (16-17 years old)

Controlled varables

Uncontroll d variablese

Zonal values Marginal HHdistributions

Multi-dimensional HHdistributions

PUMS

Population & HH

Labor Force

HH income

By size (1-9)

By workers (0-5)

By income (1-3)

By size & workers (9x6)

By size, workers &income (9x6x3)

By size, workers &income (9x6x3)

Seed

Seed

List ofsynthetic HHs

List ofHH inPUMA

-sizencome-i

-workers

-full-time workers-part-time workers-university students-non-working adults-preschool children-school pre-driving children-school driving children

%Curves 1-6+Extend 6+Adjust

%Curves 0-3+Extend 3+Adjust

Look-up table

%Curves 1-3

Discretizing

Matching

sis Procedure (MORPC) The p u

1. C bles

Figure 5.4.2. Population Synthe

op lation synthesis procedure passes through the following two main stages:

alculation of multidimensional household distribution by controlled varia for which zonal total or avera d land-use development scenario,

2. Conversion of ion to a list of households and addition of uncontrolled

ges are given as a part of the socio-economic an

the household distributvariables by matching eac ousehold to the identical household from PUMS.

At the 1

h synthetic h

st stage, the following controlled variables are specified: e (1,2,3,4,5,6,7,8,9+)

,1,2,3,4,5+) • Household siz• Number of workers (0• Income group (1,2,3)

First, a marginal distribution of ho

useholds by size is calculated. The procedure includes the following that has a positive number of households:

old size for each zone as a ratio of the household population to num

steps implemented for every zone

• Calculate average househ ber of households,

• Apply percentage curves1,2,3,4,5,6+ in each zone

to calculate percentage of households for each size category as a function of the average household size,



• Extend percentage b kbased on the look-up tab

• Adjust the distribution 1,using the entropy-maxim

Second, a marginal distrib rkers is calculated

rea down for the open 6+ category to 6,7,8,9+ categories for each zone le,

2, 3, 4, 5, 6, 7, 8, 9+ for each zone to the average household size izing method.

ution of households by number of wo . The procedure includes the following step imp

• Calculate average number of workers per household for each zone as a ratio of the employed

tion of the average number of workers per household and zonal shares of households of size 1 and 2 calculated at the previous stage,

one

Third, h ions of households by size and number of workers the pre in of households by size and number of workers

lemented for every zone that has a positive number of households:

labor force to number of households,

• Apply percentage curves to calculate percentage of households for each number-of-workers category 0, 1, 2, 3+ in each zone as a func

• Extend percentage breakdown for the open 3+ category to 3, 4, 5+ categories for each zbased on the look-up table,

• Adjust the distribution 1, 2, 3, 4, 5+, for each zone to the average number of workers per household using the entropy-maximizing method.

aving calculated the marginal distribut

lim ary two-dimensional distribution is calculated for eac o s two stages:

stribution of households in each PUMA by size 1, 2, 3, 4, 5, 6, 7, ers 0, 1, 2, 3+.

• Apply IPF procedure for each zone using zone-specific margins and seed distribution for the

h z ne. The procedure include

• Prepare seed two-dimensional di8, 9+ and number of work

corresponding PUMA. Forth, the marginal distribution of households by income group in each zone is built in three stages:

lds by income groups based on the previously tion of households by size and income applied in

n within each size-by-workers cell (look-up n is tied to the household-composition

income. Advantage of this method is that the resulting distribution of households by income is

come oup, the final three-dimensional distribution of households by size, number of workers, and income is

calculated for each zone. The procedure includes two stages:

• Estimation of the marginal distribution of househoconstructed two-dimensional distribucombination with the observed income distributiotable). Advantage of this method is that income distributiomix in each zone. Drawback of this method is that the resulting distribution of households by income is not controlled by the zonal average income forecast.

• Independent estimation of the marginal distribution of households by income groups based on the percentage curves as functions of a ratio of the zonal average income to the regional average

controlled by the zonal average income forecast income. Drawback of this method is that the distribution is not tied to the household-composition mix in each zone.

• Since the two estimation methods have symmetrical advantages and drawbacks it is proposed to calculate a weighted average of them (each of the three income group shares should be averaged across these two methods) with predetermined weights. The weights are regulated by a parameter “sensitivity to zonal income forecasts” that is scaled between 0 and 1 and represents the weight of the curve-based margins. The complementary value represents a weight for the look-up-based margins. This parameter is defined by a user in the control file.

aving calculated the marginal distributions of households by size, number of workers, and inH

gr



• Prepare seed three-dimensional distribu on of households in each PUMA by size 1, 2, 3, 4, 5, 6, 7, 8

• Apply IPF procedure for each zone using zone-specific margins and seed distribution for the

ti, 9+ ,number of workers 0, 1, 2, 3, 4, 5+, and income group (1, 2, 3),

corresponding PUMA. At the 2nd stage, three-dimensional distribution of households in each zone by household size, number oworkers, and income group is converted into the list of households

f according to the following procedure:

• Household distribution in each zone is multiplied by the total number of households and the resulting array of fractional n

umbers is discretized,

variables are added by looking up similar households from PUMS

• List of households is created for each zone by replicating each household type (feasible combination of size, number of workers, and income category) according to the discrete number in the corresponding cell of the zonal distribution.

hen the uncontrolled which correspond

o

1. Household size (truncated by 9+) 2. Number of workers (truncated by 5+) 3. Household income group (low, medium, high).

o Uncontrolled variables:

1. Number of full-time workers 2. Number of part-time workers 3. Number of university students 4. Number of non-working adults 5. Number of preschool children 6. Number of school children of pre-driving age 7. Number of school children of driving age

• For each zone and synthetic household proceed sequentially, a household from the corresponding PUMA is randomly chosen. The algorithm is as follows:

1. Define all households in PUMS as available

2. Take the first zone

3. Take the first synthetic household

4. Find an available household from the corresponding PUMA with the same controlled variables.

a. If successful, write the uncontrolled variables to the synthetic household and make the source PUMS household unavailable. If the actual PUMS household size is larger than 9 or the actual number of workers is large than 5 re-set the controlled variables (household size and number of workers) of the synthetic household accordingly.

b. If unsuccessful, but there are unavailable households that match the synthetic household, then make all households of this PUMA and this type (size*workers*income) available and go to 4.a

Tto values in the three controlled dimensions. The following procedure is applied:

• A list of sampled households is prepared in each PUMA. The preparation includes calculation of the three controlled variables and seven uncontrolled variables:

Controlled variables:



c. If unsuccessful and there is no unavailable household of thisPUMA, then change household size by -1 and number of worker

type in the s by -1

bles for 2000 were available [PB Consult, 2004]

nd Detailed Land-Use and Access Sub-Zones

The n lysis zones (TAZs) as the smallest area units repr e e most part the highway and transit level of service can be defi d -3 with time-of-day specific TAZ-to-TAZ skim matrices including

e following components (we will call them the first group):

(if adjusted household size is large than number of workers), mark this synthetic household as “adjusted” and go to 4.a

5. Take the next synthetic household and go to 4; if the list of households in the zone is up, then go to the next zone

6. If the next zone is valid, go to 3. If the list of zones is up, go to end. Several additional features and controls have been added to the population synthesis procedure for the ARC model since by the time of the ARC model development the Census Transportation Planning Package ta

Network Level of Service a

co ventional modeling approach uses transport anaes nted as centroids in the network. For thne in the same way through stages 1

th

• In-vehicle time for auto and transit, • Distance for auto and transit, • Waiting time for transit, • Number of transfers for transit, • Transit fare, • Highway toll, etc.

It is known that though a number of TAZs that can be effectively handled in network assignment procedures comes to thousands it is still not enough to represent some other important components (we will call them the second group) the following are the most important of them:

• Transit availability and walk access/egress time,

evel can create

significant aggregation biases with misleading consequences for modeling mode and destination choice.

pports most of the relevant population and land-use data (as

• Auto parking availability and cost, • Time associated with parking search and walk, • Non-motorized travel time, distance and friendliness etc.

The components of the second group can vary quite substantially within the same TAZ and require a finerspatial resolution (block, small grid cell or network link faces). Averaging them to the TAZ l

The contemporary GIS technology today suwell as geo-coded travel surveys) at the level of any analytically defined geographical unit. It can be either physical block or link-face area adjacent to the nearest network link or just a grid cell defined by,say, 50-yard step. The number of these units can come to 10,000-20,000. It is still impossible to opfull cell-to-cell matrices of this size, however, th

erate

e most parameters of the second group are vector-based by t ncost at t e from the origin to the pproc sat the finer level of spatial

he ature, i.e. they do not depend on the second end of the trip (for example, in most cases parking he destination does not depend on the trip origin; similarly, transit access tim

sto is mostly the same for all destinations). TAZ-to-TAZ attributes that are skimmed by the network es ing (like in-vehicle time) can be effectively combined with access and egress attributes specified

resolution – see Figure 5.4.3 below.



Origin stinatDe ion

2,000 TAZ’s divided into 40,000 LU & accessibility zones

2,000×2in-vehicle skim

,000

40,000×D access skim

40,00egress skim

Figure 5.4.3. Combination of TAZ-to-TAZ and Finer Cell-Level Attributes In the micro-simulation modeling framework when individ format replaces the origin-destination TAZ-to-TAZ format for travel demand, ot prin

0×O

ual-record storageadding finer geographical units does n cipally

change the model complexity. However, it ally improve the qual odel efor mode and destination choice components. Even in the case when population and land-use d known at the TAZ level only they can be reasonably apportioned by cells and it is still much better than averaging across all cells in the TAZ. Additional technical procedures have been scaling TAZ-to-TAZ attribu ll format if necessary (for example, highway distance ort trips) or differentiation of traccess/egress times by destination/origin groups in a case of several reasonab it from the same cell.

Throughout the stages 0-3 gional model development it is supposed that final demand matrices are aggregated to the condensed TAZ-to-TAZ level and assigned tandard procedure. At stage 4 it is assumed to apply a sparse-matrix technique (individual ill not require

the TAZ level.

can substanti ity of the m specially ata are

developed for for sh

tes to cell-to-ceansit

le options to access trans

of the DRCOG re

in the s assignment) that w

aggregation of trip ends to



5.5. Model System Designs and Preliminary Specifications

ile

m the research implemented in the field nd modeling practice of the several metropolitan areas (SFCTA, Portland METRO, NYMTC, MORPC, ARC) here tour-based micro-simulation model system have been successfully developed and applied. Stage 0 rresponds to the conventional 4-step model system that is used as a reference point in the description.

Accord n structural features the prop of l system ent areferred to in the following

• Stage 1: Set of Tou• Stage 2: Individual Daily Activity Pattern (DAP)

4: Time-Space Constraints

rence labels correspond to the m model design s stage. Also, each stage inherits all bas res from the previous stages.

ing to a tour-based micro-simulation tech ith only a minimal sub-set of enhancements. At the stage 1 a set of tours is modeled for each person without explicit

-househol is close to th l assumes modeling a coherent DAP for each household member however still without

ling of intra-house done in the Se 3 implies a full acco ntra-household interact l. It is

odel system desi individual DAP. It is s th

elow the model components are stratified by stages 0-4 of the model systems easy to track enhancements e in a systematic way. All e and relate to a sequence of steps in model specifications starting with basdeling struc Black ar

The modeling principles and conceptual elements described above make the tour-based micro-simulation modeling system operational. In this section we describe the model system components and their practical implementation for the DRCOG region. The description is stratified by four stages of the model system development where stage 1 corresponds to the minimal project budget and time framework wheach successive stage assumes possible model enhancement. The last stage 4 corresponds to the combination of the most advanced features available today froawco

ing to the mai osed stages the mode developm re way:

rs

• Stage 3: Intra-Household Interactions • Stage

The proposed refe ost important feature added to the comparing to the previou ic featuStage 1 assumes switch nique wactivity-basedintra-person or intrasystem. Stage 2

d interactions. This structure e design of the NYMTC mode

explicit mode hold interactions like it was FCTA and Portland METRO models. Stag unting for various i

gn. The lions and joint trave

close to the MORPC mon each component of

ast stage adds explicit modeling of time-space constraints imilar to the proposed design for the ARC mode system wi

some additional enhancements. In Tables 5.5.1-5.5.6 b

tables e eas

development, thus it ihave identical structur

from stage to stag

segmentations and mo tures and going to the details of each particular model.correspond to the absent components or features. Gray areas correspond to optional enhancements that can be made if there is a p emming fro

d Activity Segmentation

low shows the proposed base travel and activity segmentation that is applied through all ost important enhancements proposed through stages 0-4 include the

following components:

• Tour-based modeling concept from stage 1 that treats home-based and non-home based trips on

ractical reason st m the planning needs of DRCOG.

Travel an

Table 5.5.1 bethe modeling system. The m

a coherent basis as parts of a tour rather than independent trip categories.

• More detailed travel purposes for maintenance and discretionary activities from stage 3 in view of the more specific insight into the individual daily activity agenda.

• (Possible) explicitly modeling different types of in-home activities from stage 2 in view of the competition and substitution with the corresponding on-tour activities.



• (Possible) use of finer geographical units in addition to traffic-analysis zones from stage 1 for such components and transit access, parking cost and non-motorized times.

Table 5.5.1. Dimensions for Travel and Activity Segmentation

Stage 0: Stage 1: Stage 2: Stage 3: Stage 4: Tour-based micro-simulation

Components Conventional 4-step with enhancements Set of

tours Individual DAP Intra-

household interactions

Time-space constraints

TAZ 1,000-3,000 zones 1,000-3,000 or more zones

LU access sub-zones (grid ells, link-faces)

10,000-c

20,000 or more sub-zones

Usual workplace Work

Other

Grades 1-8 School

Grades 9-12

Travel purpose

University, College

Incidental Shopping

Major

Escorting

Eating out

Maintenance

Other maintenance

Leisure & sport

Visiting relatives & friends

Discretionary

Other Discretionary

Home-based From home

At work

Placement in the trip chain Non-home-based

At school, university

Work Work

Studying Studying

Home-related

Maintenance Maintenance /int-shoppingTele

Discretionary retionary Disc

In-home activity

Sleep Sleep

SOV

HOV by occu

Mode

pancy

Transit by main mode/mode combination, access mode and egress mode

Taxi

Non-motorized

TOD periods 4-5 periods (AM, Midday, PM, Night...) Departure and arrival time 1-hour resolution Continuous

Individual Individual Allocated

Fully joint Fully joint

Household & person activity / travel participation setting

Partially joint



Household Controlled Segmentation (Population Forecast)

ain household attributes that are supposed to be explicitly forecasted and d

Table 5.5.2 below shows the mcontrolled as well as the steps of the forecasting procedure including addition of the uncontrolleattributes. The last is pertinent to the micro-simulation stages 1-4 only. Several of the attributes (age ofthe household head, housing type, racial/ethnic categories) are optional and can be added if they are important in the DRCOG region. In general there should be mentioned that the base household attributesare the same for the conventional and tour-based models. Table 5.5.2. Controlled Household Attributes


Components Conventional 4-step with enhancements Set of tours Individual DAP Intra-household

interactions Time-space constraints

Household controlled segmentation (explicit forecast) Household size 1,2,3,4,5+

Household income te b3-4 ca gories by a solute thresholds

No of workers 0,1,2+

No of non-working adults 0,1,2+

No of children 0,1,2,+

Life cycle (age of thhousehold head)

e Under 35, 35-65, 65 and olde Under 35, 35-65, 65 and older r

Housing type Apartment, detached house Apartment, detached house

Family status Family / non-family Family / non-family

Racial/ethnic categories 3-4 categories depending on relevance for the region

Population forecasting procedure Zonal average targets (controlled parameters)

External data from the LU model

Zonal one-dimensional distributions by targets

Distribution curves

Zonal multi-dimensional distributions by targets

Iterative proportional fitting with seed observed distribution

Proportional distribution by sub-zones

The same distribution within the zone

Restructural

demoforecast

fully-integrate

transportatand LU mo

Shynthesizing a list of Rounding no' of households by cells ouseholds

Adding uncontrolled characteristics

Random draw from PUMS

gional

graphic or

d ion del

Household residential choice Residechoic

ntial e



Househ on Uncon eg ion tic Po )

Table 5.5.3 below shows the most fr sed al p . Ththese attributes proved to be extr t explanatory variables in travel demand models

old and Pers trolled S mentat (Synthe pulation

equently uemely importan

addition household and erson attributes ough ,

ere explicit forecast. Population synthesizing procedure allows for making up these onal values and di

at since the concep is no any dditional attrib es are used. Any attribute can be added if it

mple and there are enough households in the corresponding PUMA ute (for example specific eth

o the micro-simulation framework, adding uncontrolled attributes is a form of using eful even in rath

categories. This stru t has been successfully used M.

ttributes

it is difficult to produce thparameters though there average ztargets. It should be noted th

stributions are not controlled by any predetermined t of synthetic population has been adopted there

principal difference in how m a ut is presented in the source (PUMS) sathat possess this attrib nic group). Though specific tPUMA proportions can be usworkers by individual earning

the conventional modeling scheme. For, example stratification of er than by household income can improve the model quality for

prediction commuting flows by earning in the New York BP

ctural componen

Table 5.5.3. Uncontrolled Household and Person AStage 0: Stage 1: Stage 2: Stage 3: Stage 4:

Tour-based micro-simulation

Components Conventional 4-

step with enhancements

Set of tours Individual DAP Intra-household interactions


Household attributes No of full-time vs. part-time workers

0, 1, 2+ / 0, 1, 2+

Earning for workers 0,1,2+ by 3-4 categories 0, 1, 2+ by 3-4 categories

No of students vs. homemakers and retired

0, 1, 2+ / 0,1,2+

No of preschool children (under 6) vs. school children of pre-driving (6-15) and driving (16-17) age

0, 1, 2, 3, 4+ / 0, 1, 2, 3, 4+

Person attributes Sex Male, female

Occupation/education category 3-4 base categories 10-20 categories

Age Any categories

Worker Full-time worker, part-time worker Person type

Non-worker University student, homemaker, retired

Child Preschool (0-5), pre-driving (6-15), driving (16-17)



La menta

The variables requir th r a cludemployment by branch, school/college/university enrollment and area type egoriz

elow). Addition ts) can btivity types (m

There is also a gradual disa hool enrollment segmentation from stage 2 of kdown by industrial, commercial, office and other branches it ll retail and

stores, public (go edical sellment detailed by grades can bet g of act

patterns.

Table 5.5.4. Land-Use Segmentation

nd-Use Seg tion

ed are more or less e same as fo the convention l model. They in/density cat

e ation (see

Table 5.5.4 bfor the appropriate actarget years.

al variables (like gross floor area or no of theater seaaintenance, discr

e considered etionary) if they can be supported for the base and

ggregation desired in the employment and sc the model development. In addition to the usual employment brea

is beneficial to distinguish between smalarge-scaleSchool enro

vernment, municipal) and other offices, single out m rvice etc. ivity-travel ter support the household-side modelin

Stage 0: Stage 1: Stage 2: Stage 3: Stage 4: To -based micro-simur ulation

Components Conventional 4-step with enhancements Set of tours Individual

DAP Intra-household


Employment by branch se ca3-4 ba tegories 10-12 categories 10-20 categories

School/college/university enrollment

2-3 base ca 5-6 categories s tegories 5-6 categorie

Area type/density 5-6 categories

Household Automobile Ownership

own is similar for conventi ulation stage system development. This model takes a form of discrete choice

Household automobile models up to the

ership model 3 of model

onal and micro-simtour-based

amongst alternatives representing a number of cars o s a function of the household composition and other characte ousing type, occupation, lif

inly mandato een found thatof accessibility measures with and without auto mod t quality takes a ) can be a strong determinant of the h nership. The choice mo it,

t and stati l show which of them performs better the main features household automobile ownership m

n important enhancement proposed for advanced stage 4 of the model system development is vehicle ratification by 5-6 vehicle types

wned by a household aristics (h e cycle), area type and

accessibility to (ma ry activity) destinations. In particular it has b comparison major rolee (without the last transi

ousehold car ow del can be multinomial lognested logit or ordered logibelow summarizes

stical analysis wil of the proposed

. Table 5.5.5 odel.

Ast (size class and vintage). The corresponding choice models have been alre ymodels. use alon be availabl

ad developed and applied in practice, however, never in the context of full-scale regional demand This additional segmentation will match the concept of explicit tracking each car allocation andg with explicit tracking persons. As a result a detail structure of vehicle flows by types will

e that is important input for environmental analysis and traffic-engineering projects.



Table 5.5.5. Automobile Ownership Model Stage 0: Stage 1: Stage 2: Stage 3: Stage 4:




Set of tours

Individual DAP Intra-household interactions


Choice alternatives No of autos for each household No of automobiles owned by household (0,1,2,3,4+) Type of each household auto Vehicle types (5-6)

Variables in the model structure Relative car-sufficiency No of cars versus no of workers / adults / drivers

Household/person characteristics Controlled Controlled & uncontrolled

Area type/density Area-type stratification or dummy

Accessibility to destinations by Stratified log-sums/accessibility indices over destinations wimodes

th and without drive alone

Choice alternatives No of cars No & type of cars

Travel Model System Hierarc

odels applied at each stage of the model systflects a basic de mpositi of the travel choic

ca ng, applying long-term choices, day-level choic ending with network processing (route choices). Various choice models (multinomial logit,

it, etc will be statistically examined at each level of the ch ce hierarc

hy

The proposed combination of choice mshown in Table 5.5.6 below. It

em development is es by natural hierarchy, re co on

starting with population forechoices, and

sti es, tour and trip-level

nested logit, ordered log

.) oi hy.

Stage 1 of the model system development will have basic tour-based and micro-simulation features including (possibly) synthesizing population with a wide set of (uncontrolled) attributes and (possibly) fine geographical sub-zones, however it will not yet have long-term set of choices, over-arching daily activity-travel pattern, or intra-household interactions. Stage 2 will have individual activity-travel pattern type as an over-arching model preceding generation of a set of tours. Stage 3 will have a full set of intra-household interactions and long-term choices of location for mandatory activities (work/school/university). The most advanced stage 4 will include a full 24-hour tracking each individual activity and vehicle allocation with a full set of realistic constraints in time and space. It will also incorporate residential choice and vehicle type choice for each household. Several modeling components are flexible and can be either applied at the early stage 1 or postponed to the following stages if the corresponding estimation and/or application will prove to be difficult in view of the limited size of the current household survey for the DRCOG region. This relates to the TOD time windowing that avoids double-scheduling of tours and trips of the same person, however, requires ordering of tours by importance (not necessarily sequencing or daily activity pattern). Another flexible component is explicit treatment of joint tours that can be done better from stage 3 as a part of coordinated activity schedules of several household members, however, can be promoted to early stages 1-2 in a simplified version. Additional flexible modeling feature that can be finalized only after statistical analysis and model estimation relates to the modeling exact number of stops at each half-tour. There are three following possible structures that will be examined statistically:



Table 5.5.6. Main Choice Model Hierarchy Stage 0: Stage 1: Stage 2: Stage 3: Stage 4:


Components Conventional 4-step with enhancements Set of tours Individual DAP Intra-household


Population distribution by zones and household segments Total population External

Employment distribution by zones and branches

School/Co sity e is zone ches

co

llege/Univer nrollment d tribution by s and bran

mponents (inputs)

Access sub-zone proportions within zones

Household m sion eulti-dimen al distribution for each zon

old synthesis by co d targHouseh ntrolle ets

Population forecasting

Uncontrolled Uncontrolled characteristics

naemoforecast

Regiod

l structural graphic

Residential choice

Ho ehold auto ownershus ip

Vehicle type

Regular workplace

Long-term choices

R y egular school/college/universit

Individual activity-pattern type

Trip production Linked tour frequencies

Individual DAP Coordinahouseh

ted DAP’s and schedules for all old members

Joint activity episodes and travel

Day-level choices

Car allocation

TOD rrival time Departure & a

Tour primary destination

Tour main mode / mode combination

Tour-level choices

Stop frequency by half-tours (journeys)

Destination cation Stop lo

Detailed trip mode

Trip-level choices

TOD Departure & arrival time

Aggregate TAZ-to-TAZ simulation and skimming

Detailed sub-zone access

Sparse-matrix Network processing simulation

TOD simulation Hourly/dynamic simulation

Vehicle movement simulation

Aggregate flows Individual Individual by type

• Daily activity-travel pattern (applied at stages 2-4) will include exact number of stops for each tour by the half-tours (i.e. all secondary activities); this will exclude the stop-frequency choice model at the tour level. This approach is appealing in view of the coherent considering all activities, however, it misses spatial and travel-environment impacts on stop-making.

• Daily activity-travel pattern will include only a binary indicator (no stop vs. at least one stop) on each half-tour, with a subsequently applied stop-frequency choice model that predicts exact number of stops (1, 2, 3, …) on each half-tour that has obtained “at least one stop” feature in the activity-travel pattern; however, this exact prediction will be based on known (modeled) primary destination and entire-tour mode.



• Only primary tour will be modeled with stops at the activity-travel pattern stage while all with a full set of stop-

e and destination

s.

able 5.5.7 below summarizes the proposed staging of this modeling component. The explicit prediction f the regular workplace/school/university

secondary tours will have a stop-frequency choice model (operating nowfrequency combinations by half-tours including zeros) applied after tour modchoice. Thus only trade-offs between stops on the primary tour and secondary tours will be captured in the daily pattern, while secondary-tour details will be modeled separately by tour

Long-Term Choices of Location of Mandatory Activities

To for all household members is beneficial in view of the impact on the daily activity pattern; thus, it should be a part of stage 3 (or even 2) of the model system development. At stage 4 it will be combined with the re hoisidential c ce to create a co delingsystem for all base spatial components of the individual b

of Mandatory Ac on

herent mo (pivot points) ehavior.

Table 5.5.7. Long-Term Choices tivity LocatiStage 0: : Stage 2: Stage 1 Stage 3: Stage 4:




Set oftours



Household/person characteristics Con trolled & uncontrolled

Accessibility to / availability odestinations

f Log-sums over destinations

Linkage across household members

Joint with residential



Individual Activity Pattern vs. Set of Linked Tour-Frequency Models

Table 5.5.8 below shows details of the travel (tour, trip) generation group of models at the different stages of the model system development. Table 5.5.8. Different Tour-Generation Modeling Structures




Set of tours Individual DAP

Intra-household interactions


Individual activity pattern type Household/person characteristics Controlled & uncontrolled


Regular workplace/school/college/university

Mandatory travel budget

Accessibility to other destinations Log-sums/accessibility indices

Choice alternatives Main activity combination

Linkage across household members Intra-household priority, joint and allocated activity

Individual activity pattern (daily set of trips/tours) Household/person characteristics Controlled Controlled & uncontrolled


Regular workplace/school/college/university


ain activity combination Activity-pattern type M

Accessibility to destinations Stratified log-sums/accessibility indices over destinations and TOD

Choice alternatives Trip frequency Tour frequency Sequence of scheduled tours

Joint travel Fully joint trips/tours Fully joint tours Full & partial joint

Linkage across household members Person/purpose/joint priority Person/pattern/purpose/joint priority

It should be mentioned that stage 1 is actually very similar to stage 0 (conventional model system) with the exception of using tours instead of trips as base units of analysis and modeling. Stage 2 already has a principal enhancement in the activity-pattern type over-arching a set of tours and thus, ensuring a better linkage across tour-frequency models. Stage 3 introduces coordinated activity-travel patterns and schedules for all household members. Stage 4 adds also partially joint tours (pick-ups, drop-offs of passengers) as an explicit component.



Upper-Level Tour Sequencing/Scheduling/Time of Day Choices

Table 5.5.9 below summarizes base features of the tour sequencing/scheduling/TOD choice model. An important general advantage of all tour-based models starting from stage 1 is a consistency of TOD choices across trips within the same tour that never can be achieved with a trip-based model. Stages 1-2 operate with TOD-period resolution for each tour that ensures a partial consistency across tours in view of the “crude” scheduling rules. At stages 3-4 a finer temporal resolution (hour) is used that allows to fully control scheduling and avoid conflicting time choices. Table 5.5.9. Timing Choices Modeled Before Mode and Destination Choice




Set of tours





Regular workplace /school/college/university


Activity-pattern type Main activity combination

Activity pattern Trip frequency Tour frequency Sequence of scheduled tours

Accessibility to destinations Log-sums/accessibility indices over destinations for each TOD

Activity-duration utility Activity-participation utilities as functions of the derived duration

Choice alternatives TOD for each trip TOD for each tour and trip Departure & arrival time combinations

Consistency across trips within the tour Full

Consistency across tours of the same person

Partial Full

Synchronization of tours made jointly by several persons

Fully-joint tours Fully & partially joint tours

Mode and Destination Choice

Below is a set of models covering all aspects of mode and destination choices at the entire-tour (Table 5.5.10) and detailed-trip (Table 5.5.11) levels. In a similar to the TOD choice way, one of the inherent advantages of the tour-based models starting from stage 1 is a full consistency of mode and destination choices for all trips within the tour. With respect to a consistency across tour primary destinations of the same person, there is a partial account for available destinations within each tour time window at stage 2 (because of the comparatively “crude” TOD temporal resolution). From stage 3 to 4 there is a full consistency across spatial choices of the same person achieved. Mode choice consistency across tours of the same person is partially controlled at stages 1-2 with respect to a correspondence of at-work tour mode to the mode chosen for the work tour while modes for the other tours are still modeled independently. At stages 3-4 there is a full consistency across tours based on controlling household car allocation.



Table 5.5.10. A Set of Mode and Destination Choice Models (Tour Level) Stage 0: Stage 1: Stage 2: Stage 3: Stage 4:




Set of tours Individual DAP

Intra-household interactions


Pre-mode choice Household/person characteristics Controlled Controlled & uncontrolled

Area type/density/pedestrian friendliness Area-type stratification or dummy

Non-motorized accessibility to destinations Log-sums/accessibility indices over destinations in the 3-mile radius

Motorized accessibility to destinations Log-sums/accessibility indices over destinations for each TOD

Choice alternatives (upper level) Motorized/non-motorized binary mode

Choice alternatives (lower level) Destination if non-motorized mode is chosen

Consistency across trips within tour Full

Tour primary destination choice Household/person characteristics Controlled Controlled & uncontrolled

Regular workplace/school/college/university Regular vs. other

Activity-pattern type Main activity combination

Activity pattern and available time windows Tour frequency limitation Time-space constraints

Accessibility to destinations for the chosen TOD

Log-sums/accessibility indices over main modes for the chosen TOD

Land-use attraction (size variables) Attraction as a function of land-use variables

Choice alternatives Detailed access sub-zones

Consistency across tours of the same person Partial Full by travel budget

Tour main mode / mode combination Individual vs. joint setting Fully-joint Fully / partially

Household/person characteristics Controlled Controlled & uncontrolled Individual car availability for the person Individual

priority Car allocation

The chosen mode to work for at-work sub-tour Car un-availability for transit and shared-ride commuters

The chosen primary destination characteristics Parking cost, access time

Travel time, cost and other mode characteristics

Time & cost components, reliability, comfort, convenience, safety

The chosen activity pattern Predetermined stop frequency

Stop-making opportunity (stop-density on the way)

Stop-frequency log-sum

Choice alternatives Entire tour-related mode combinations

Consistency across household & person tours Work / at work only Controlling car allocation

Stop-frequency choice (exact no of stops on each half-tour) Household/person characteristics Controlled Controlled & uncontrolled

The chosen main tour mode Entire tour-related mode combinations

The chosen activity pattern Other tours chosen Predetermined stop frequency

Stop-making opportunity for the chosen destination

Stop-location log-sum

Choice alternatives Stop-frequency combinations by journey directions

Consistency across person and household tours

Trade-offs with other tours

Trade-offs with other tours and stops



Table 5.5.11. A Set of Mode and Destination Choice Models (Trip Level) Stage 0: Stage 1: Stage 2: Stage 3: Stage 4:




Set of tours Individual DAP Intra-household interactions


Stop-location choice Household/person characteristics Controlled Controlled & uncontrolled

The chosen main tour mode The chosen entire tour-related mode combination

Land-use stop-attraction (size variable)

Stop-attraction as a function of land-use variables

Relative location of origin and primary destination

The chosen primary destination location relative to the tour origin

Route deviation (combined impedance)

Route deviation or combined impedance (trip-mode choice log-sum)

Choice alternatives Detailed access sub-zones

Detailed trip-mode choice Household/person characteristics Controlled Controlled & uncontrolled

The chosen main tour mode The chosen entire tour-related mode combination

The chosen trip modes for previous trips in the tour

The chosen trip modes for previous trips (journey) in the tour

The chosen destination/stop characteristics

Parking cost, access time, activity type (individual, escorting, joint)

Travel time, cost and other mode characteristics

Time & cost components, reliability, comfort, convenience, safety

Choice alternatives Available modes and mode combinations

Consistency across trips of the tour Full consistency

Lower-Level Trip Time of Day/Departure/Arrival Time Choice

Table 5.5.12 below shows the main features and components of the trip-level time choice model. The principal consistency across trips within the same tour is ensured from stage 1 because it is inherent to the tour-based concept. From stage 3 a finer spatial resolution (hour instead of TOD period) is proposed that will enhance behavioral realism and internal consistency of this travel dimension. Table 5.5.12. Trip Time Choice Models




Set of tours




The chosen TOD for the tour Time window available for the trip

Area type Area-type stratification or dummy

Time and cost for the chosen mode and destination

Time & cost components, parking convenience

Activity-duration utility Activity-participation utility as a function of the derived duration

Choice alternatives TOD for each trip Departure & arrival time

Consistency across trips of the tour Full consistency within TOD period

Full consistency at 1-hour resolution



Model System Integration via Accessibility Measures

This sub-section relates mostly to the advanced stages 3-4 of the model system development. However, some of the options listed below can be included into the simplified versions of stages 1-2 based on the statistical validity of the log-sum (or other accessibility) measures in the calibration procedure. There are multiple cases in the choice hierarchy where carrying up log-sums from the lower-level choices is beneficial. Actually, carrying up lower-level log-sums would have been a good idea for every choice level had it been computationally doable. As spatial dimension is the most difficult, it is worth trying sampling strategy to alleviate the computational burden. Several general observations should be made before we consider a set of log-sums to include in the modeling structure:

• The model structure contains several evolutions of the choice unit and the corresponding decision-maker (household-person-tour-journey-trip). Thus, in several cases when the desired log-sum measure pulled up from the different decision-making unit a theoretical issue arises of the nature of the included measure in the context of the more general behavior unit. In particular, in several cases there can be several log-sums from the lower-level included into the upper-level choice (for example, person activity-pattern model can include several tour log-sums). It can be shown that a choice structure with several log-sums stemming from the decision-unit split is a valid random utility model; however, differential coefficients for different log-sums create additional nesting levels that should be taken into account in the model estimation.

• There is no contradiction in simplifying accessibility measures for upper-level choices where carrying log-sums from the lower levels may prove to be cumbersome even with sampling simplifications. Using surrogates instead of exact log-sums just means that the model system looses the linkage across levels in a sense of a simultaneous choice of alternatives by all travel dimensions. However, it is probably even more realistic to assume that people make travel-related choices sequentially with differential reconsidering of available information and decision-making rules. From this point of view a fully-elegant multi-level choice structure with perfect log-sums (i.e. one big nested structure covering all travel dimensions) would look suspicious. As a matter of fact, not log-sums and a formal linkage of choice levels is a purpose in itself, rather a desire to make upper-level activity choices reasonably dependent of travel “implement-ability” that can be screened up only at the lower levels. Another and related aspect is a desired sensitivity of activity patterns to network improvements and policy measures. In practical terms, any reasonable, statistically-significant and network-policy-sensitive measure of accessibility for upper level choices can be adopted instead of formal log-sums from the lower levels.

• The log-sum technique is essentially derivative from the fractional-probability choice models. Since the discrete choice model assigns a positive probability to any (even the worst) alternative the log-sum measure that is an average utility across all alternatives has to be calculated taken into account the whole range of alternatives. In a micro-simulation framework, where a single discrete choice is finally made explicitly it is logical to consider this only choice as a reasonable representative of the whole range of alternatives. Then an interesting alternative technique suggests itself where the linkage across different levels of the choice hierarchy is modeled by the chosen lower-level alternative only. It does not impose any problem in calibration (just instead of the full log-sum a single utility of an observed chosen lower-level alternative is included or even a particular attribute like travel time or distance). However in application it greatly simplifies calculations because there is no need to calculate log-sums, just to choose representatives. A sort of an iterative procedure has been applied several research projects where only at the beginning for the first upward movement through the choice hierarchy the whole-dimensional calculation of probabilities is needed (say mode-choice probabilities for each destination) in order to pick up representatives for each upper-level choices (we call it “mapping travel accessibilities”). Then, after the upper-level choices have been made, the lower-level choices are re-simulated downward only for the chosen upper-level alternatives (we call it “travel realization



of activities”). Then traffic and transit simulation is applied to obtain new skims. Then instead of the full recalculation of the travel accessibilities only the chosen alternatives (mode & destination) are updated for the chosen upper-level activity pattern (we call it “recognition of the travel consequences”) while the alternative activity patterns hold the old travel attributes. Then another iteration of choices downward is implemented etc. The procedure is more effective than a log-sum calculation and avoids any restrictions on the number of lower-level attributes carrying upward. It also proved to be effective in terms of global convergence.

The following log-sum relations (or other forms of lower-level-related accessibility attributes) should be included into statistical analysis:

• Trip mode choice log-sum (or simply the chosen-mode travel time) to stop-location model.

• Stop-location choice log-sum to tour main mode choice; interesting that application of the “chosen representative” technique allows for simply inclusion of the chosen stop location into the mode utilities explicitly (for example, instead of “i to j” travel time it should be combined “i to j through k” time). In many respects it is much more realistic that abstract log-sum across stop-locations.

• Tour main mode choice log-sum (or simply the chosen weighted travel time or mode utility) to primary-destination choice.

• Tour primary-destination choice log-sum (or simply the chosen-destination travel time, scaled distance or the chosen mode & destination utility) to the person activity pattern choice. If standard log-sum technique is applied it is necessary to include TOD choice between activity pattern and tour-destination choice. Otherwise it is not quite clear what time period should be used in the upward direction. However, in the “representative” framework we can jump over TOD dimension using mode & destination utility for the chosen period of a day.

• Work, school and university tour log-sums with intermediate stops over all periods of a day to person activity pattern type, and participation in joint tours.

• Tour log-sum over periods of a day to joint tours’ generation. Intermediate stops are important here because they reflect density of maintenance and discretionary activities on the way, and joint household tours (that are mostly maintenance and discretionary by nature) are quite frequently multi-destination.

• Person activity pattern log-sum is proposed to be included in pattern type and participation in joint tours’ generation. Actually, it can be shown that if the structures of activity patterns are fixed and there sets are fixed for each pattern type (that is the case) than only the tour-related accessibility parameters are matter for joint tours. Other pattern and participation-related attributes will be incorporated in the patter-type-specific constants. Thus, there is a choice of ether direct inclusion of tour log-sums into the pattern-type choice (as was discussed above) or through activity-patterns (not both of them). We can check both hypotheses statistically.

• Activity-pattern-type log-sum is proposed to be included into the long-term choices of mandatory activity locations. It is based on assumption that all tour-destination accessibilities will play a role through activity-type choice. However, in reality long-term choices are frequently made based on a crude estimation of the accessibility to main mandatory activities where details of secondary activities are not taken into account in full. Additionally, this log-sum is the most complicated because it requires storing of all pattern & tour utility combinations (sampling destinations can make it more practical). We will also consider simplified measures like minimal travel time of all modes in conjunction with additional impedances and “density-on-the way measures”.



Examples of Regional Model System Design

Table 5.5.13 below indicates which of the most important model features were included in the already developed model systems. Figure 5.5.1 below presents the latest modified model system hierarchy adopted for the ARC project. Table 5.5.13. Tour-Based Model Systems Developed for Metropolitan Planning Organizations

Feature Portland (METRO)

San Francisco (SFCTA)

New York (NYMTC)

Columbus (MORPC)

Atlanta (ARC)

Consistent generation of all tours and trips made during a person-day?

Yes Yes Yes Yes Yes

A full population stochastic micro-simulation framework?

No in 1st version, Yes in later

versions

Yes Yes Yes Yes

Explicit modeling of interactions between activity patterns of household members?

No No No Yes Yes

Greater spatial detail than the TAZ level for land use and walk/transit access?

No in 1st version, Yes in later

versions

No No No Yes

Greater temporal detail for activity and travel scheduling?

Somewhat (5 time periods)



Yes (1 hour periods)

Yes (1 hour periods)



Person Pattern Type--primary activities--at-home or on-tour--work/school destination--work/school time period

Household Activities--joint tours --number and purpose --participation by HH subsets--maintenance activities --number --allocation to individuals

Person Pattern--extra stops--secondary tours--at-home maintenance

Tour--detailed purpose--time periods--destination and mode

Stop--Purpose--location--trip mode & departure time

--one per person--conditioned by pattern type of higher priority persons

--one per person--conditioned by pattern of higher priority persons

--one set of tours per person--conditioned by stops of higher priority tours

--one set of stops per tour--locations conditioned by stop priority--mode & time conditioned by temporal sequence

--one set per household

Figure 5.5.1. Current Proposed Household Activity and Travel Model System for ARC



5.6. Overview of the Model System Enhancements and Data Requirements from State to Stage

From the Conventional Four-Step Model (Stage 0) to “Set of Tours” (Stage 1)

The following positive modeling features should be mentioned:

• Stage 1 is similar to stage 0 (conventional model system) in several respects with the main exception of using tours instead of trips as base units of analysis and modeling. Stage 1 of the model system development will have basic tour-based and micro-simulation features including (possibly) synthesizing population with a wide set of (uncontrolled) attributes and (possibly) fine geographical sub-zones, however it will not yet have long-term set of choices and over-arching daily activity-travel pattern.

• Tour-based modeling concept treats home-based and non-home based trips on a coherent basis as parts of a tour rather than independent trip categories. Explicitly modeling trip chains with intermediate stop frequency and location on half-tours (journeys) ensures consistency across all travel dimensions of the trips within the same tour.

• Hierarchical modeling mode choice is applied with, first, modeling an entire-tour decision on a principal mode combination and then modeling each trip detailed mode choice conditional on the tour mode. This ensures a full consistence of mode choices for all trips within the tour

• Another important general advantage of all tour-based models starting from stage 1 is a consistency of TOD choices across trips within the same tour that never can be achieved with a trip-based model. Stages 1-2 operate with TOD-period resolution for each tour that ensures a partial consistency across tours in view of the “crude” scheduling rules.

• Another inherent advantage of the tour-based models starting from stage 1 is full consistency of primary destination and secondary stop locations for all trips within the tour.

• Intra-household dependencies in tour-frequency choices are introduced based on a pre-defined, rational priority hierarchy. This would require enormous segmentation by trip-frequency combinations in the conventional fractional-probability framework.

• Mode choice consistency across tours of the same person is partially controlled at stages 1-2 with respect to a correspondence of at-work tour mode to the mode chosen for the work tour while modes for the other tours are still modeled independently.

• Synthesizing population allows for addition of numerous uncontrolled household and person attributes. In particular such important explanatory variables can be added as employment status for workers (full-time vs. part-time) and age (school grade) categories for children.

• Using fine geographical units in addition to TAZ allows to model transit access/egress, parking availability, cost and walk as well as for non-motorized travel time and distance.



From “Set of Tours” (Stage 1) to “Individual Daily Activity Pattern” (Stage 2)

The following additional positive modeling features should be mentioned:

• Stage 2 has a principal enhancement in the activity-pattern type over-arching a generation of a set of tours and thus, ensuring a better linkage across tour-frequency models. This is a first step towards the daily activity schedule in order to avoid a long sequence of conditional models that prevents from capturing the effect of activity opportunities in lower-level model dimensions (secondary tours and stops) upon upper-level choices (primary tours). However, in view of the large number of possible activity-schedule combinations, it is necessary to avoid a “flat” multi-dimensional structure that differentiates lower-level alternatives primarily by alternative-specific constants and hides important error correlation among similar alternatives. Thus, it is recommended to start with an over-arching activity-patter-type model at stage 2 in combination with a conditional set of journey-frequency models and gradually add dimensions at stage 3 until the whole spectrum of journey frequency combinations is exhausted.

• Explicitly modeling intra-household joint tours where two or more members of the household travel together for an entire tour for the same purpose. This allows for a proper linkage of the destination and mode choice decisions for the all involved household members. This option at stage 2 currently relates to fully joint tours only while more complicated partially joint tours (with intermediate passenger pick-ups and drop-offs) are not explicitly modeled until stage 4. However, passenger pick-ups and drop-offs are included as an individual maintenance sub-purpose. They just are not explicitly linked to the corresponding passenger destination and mode choice. A simplified version of joint travel can be included even into stages 0 and 1. However it is more natural and effective in combination with an activity-pattern fragment pertinent to a stage 2 and higher.

• There is also a gradual disaggregation desired in the employment and school enrollment segmentation from stage 2 of the model development.

• With respect to a consistency across tour primary destinations of the same person, there is an account for available destinations within each tour time window at stage 2, however, it is still partial because of the comparatively “crude” TOD temporal resolution.

• (Possible) explicitly modeling different types of in-home activities from stage 2 in view of the competition and substitution with the corresponding on-tour activities.

From “Individual Daily Activity Pattern” (Stage 2) to “Intra-Household Interactions” (Stage 3)


• Stage 3 introduces important intra-household interactions that have a crucial impact on the formation of individual daily activity-travel patterns of all household members. These enhancements include coordinated modeling of daily pattern types, explicit modeling of joint activity and travel episodes, as well as intra-household allocation mechanism for maintenance activities.

• Modeling regular work/university/school locations of mandatory activities as long-term choices. These locations relative to the residential place and the corresponding mandatory part of the daily travel budget of the individual are important determinants of the whole daily activity pattern. The explicit prediction of the regular workplace/school/university for all household



members is beneficial in view of the impact on the daily activity pattern; thus, it should be a part of stage 3 of the model system development

• Refining broad categories of maintenance and discretionary travel into more homogenous sub-purposes. More detailed travel purposes for maintenance and discretionary categories from stage 3 in view of the more specific insight into the individual daily activity agenda. Maintenance sub-purposes are likely to be shopping, escorting passengers, eating out and other. Discretionary purposes are likely to be leisure & sport, visiting relatives & friends and other.

• Using age categories to differentiate behavior of children under 18. It has reflection on mode choice (small kids require more escorting or school bus, while teenagers can use regular transit) and destination choice (high schools normally allow for wider districts).

• At stages 3-4 a finer temporal resolution (hour) is used that allows to fully control scheduling and avoid conflicting time choices

• From stage 3 there is a full consistency across spatial choices of the same person achieved by means of controlling spatially available choices within time window of each tour.

From “Intra-Household Interactions” (Stage 3) to “Time-Space Constraints” (Stage 4)


• The most advanced stage 4 will include a full 24-hour tracking each individual activity and vehicle movement with a full set of realistic constraints in time and space.

• The explicit prediction of the regular workplace/school/university for all household members that is beneficial in view of the impact on the daily activity pattern; thus, it is a part of the previous stage 3 of the model system development. At stage 4 it will be combined with the residential choice to create a coherent modeling system for all base spatial components (pivot points) of the individual behavior.

• Household vehicle stratification by 5-6 vehicle types (size class and vintage) is proposed. This additional segmentation will match the concept of explicit tracking each car allocation and use along with explicit tracking persons. As a result a detail structure of vehicle flows by types will be available that is important input for environmental analysis and traffic-engineering projects.

• Stage 4 adds partially joint tours (pick-ups, drop-offs of passengers) as an explicit component.

• At stage 4 there is a full consistency of mode choices across tours made by all household members based on controlling household car allocation.

• Individual-vehicle and dynamic assignment is proposed instead of the aggregate zone-to-zone assignment.



5.7. Model Design Conclusions and Recommendations The following conclusions and practical recommendation regarding the model system development for the DRCOG Modeling Team can be made:

• The new model system development is proposed to begin with a framework of stage 1 that corresponds to the current project budget and allows for gradually adding features at the more advanced stages 2-4 depending on the practical needs of the DRCOG modeling team.

• Stage 1 modeling system based on the tour concept and micro-simulation can be implemented in parallel with the corresponding conventional model that will have the same segmentation and similar modeling structure. It will allow comparative analysis of the new modeling methodology before it can be finally recommended for practical use in DRCOG.

• Though the propose staging of the model system development from stage 1 through 4 is preferable from the general point of view, there is a good deal of flexibility in the combination of the modeling components. In particular, some components from advances stages 3-4 can be optionally included into the prior stages 1-2 with certain simplifications if they are important in view of the practical planning needs of the DRCOG modeling team. In particular, planning new mass transit modes (commuter rail lines, LRT, subway) with possible mode combinations (park-and-ride) will require advanced features of the mode-choice model from the very beginning of stage 1 while tour-generation stages can still be implemented without advanced daily travel-activity pattern component. In a similar way, if congestion-relief measures include TOD-period-specific toll or parking cost strategies, the TOD choice model should be enhanced from the very beginning of stage 1 in order to serve practical planning needs. The modular principle of model system development allows for such flexibility in terms of the model estimation and calibration and the corresponding software development for application. It is proposed to organize several working sessions with the DRCOG Modeling Team in order to finalize the modeling structure and staging of the project.

• New household survey should be considered to support enhanced versions of the modeling system in combination with the model development and application in the stage 1 framework.

• A series of workshops may be needed with the DRCOG modeling team, where details of the modeling structures and data requirements will be finalized in view of the practical planning needs of DRCOG.



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SECTION 6

DATA INVENTORY AND MATCH TO

STRUCTURAL ENHANCEMENTS


6. Data Inventory and Match to Model Structural Enhancements

The purpose of this section is to provide a data assessment for the next-generation DRCOG travel demand model. It serves as a base to match the data requirements for the development of a state-of-the-practice regional model, with the adequacy and effectiveness of model-related data that are currently possessed by or potentially available to DRCOG. It is assumed here that both the trip-based and the tour-based modeling frameworks are candidate approaches to be considered in the update of the IRM. Thus the assessment will cover the data requirements for the development of either a tour-based model or a trip-based model. This section is organized in five parts. Following this introduction, the modeling relevant data currently in DRCOG’s possession, regularly received by or potentially available to DRCOG are reviewed and catalogued. The data collection dates, collection methods, sample sizes, their general functionalities of the identified datasets will be discussed. The third part provides an overall assessment of the cataloged datasets for the development of a state-of-practice regional travel demand model, followed by the assessment of the data to match the specific structural enhancements of the model as identified in this study. The section concludes with recommendations for additional surveys and data collection activities to be conducted to support the enhancement of the DRCOG regional model.

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6.1. Data Catalog There are a variety of datasets available for the update of the DRCOG model. These include the data that are maintained and routinely updated by DRCOG and other agencies in the region (inventory data), and data collected recently from specific surveys (survey data). In the rest of this section, these two types of data will be reviewed and catalogued, with discussion covering the data collection dates, data collection method, sample sizes, overall functionality and their general strengths and weaknesses.

Inventory Data

DRCOG and other agencies in the study region maintain various inventory datasets that would be useful for model development. Table 6.1.1 presents a list of these inventory data. Table 6.1.1. Inventory Data Catalog

Type of Data Size/Level Of Detail

Transportation System Data

Current DRCOG model-based network data Regional network, excluding neighboring streets and sidewalk

Roadway network database Regional highway network covering all level of facilities

Traffic volume data

Speed data

Transit service operation data RTD services at route level

Transit patronage data RTD bus at route level, LRT at station level

Land Use and Demographic Data

Population household data TAZ level in 5-year intervals

Employment data TAZ level in 5-year intervals

School enrollment data

Commodity Flow Data

TRANSEARCH Dataset Nationwide and cross-border

Model Network Data

The existing DRCOG model (recently converted from MinUTP platform to TransCAD platform) contains both highway network and transit network data. The highway network (highway link layer) includes the following basic link attributes:

• Distance • Facility type • Direction • Number of lanes • Toll link or not • HOV restriction • Speed (optional) • Traffic count volume (if available)

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The highway network contains basic roadway physical information for motorized vehicle traffic. It lacks more detailed operational information of the roadway facilities or information regarding non-motorized facilities. The following additional link attributes should be considered in the network:

• Toll policy • Ramp metering • Delays at crossings & toll plazas • Signal control (e.g., % green time) • Roadside development attribute (driveway density or signal density) • Roadside parking • Available Bike/walk path facility

Also, the highway network was originally developed under the MinUTP platform. It is not a GIS-based network. In the next generation model, truly GIS-based network (link layer) can be developed under the TransCAD platform. The transit network (route layer) contains transit route data such as headways, stops of each route or route pattern. Locations of park and ride lots and transit centers are also coded in the transit route layer. However, the transit network doesn’t include more detailed operational information for the park and ride lots, such as size, parking charge, capture area, walking distance to station entrance, etc. Roadway Network Database

The DRCOG Roadway Inventory Dataset maintains very detailed geometric information of the roadway network in the DRCOG region, such as alignment, number of lanes, lane width, existence of bicycle or walk path, intersection layout, types of control at intersections, etc. This database provides most of the data for updating the DRCOG model network. Relevant GIS-data

DRCOG maintains extensive GIS-based data in various layers. These include the base roadway layer and boundary layers of various geographical levels. DRCOG also developed a walk/bike path layer, which can be use as a base to develop a walk/bike network. It is important that the model related data and survey data be linked to or implemented under a GIS system to facilitate the management, display and analysis of the data at various stages of the model development, as well as for producing forecasts in support of DRCOG’s activities. Traffic Data

Numeric traffic volume data are available in the region, including road segment counts, intersection turning movement data, and external station traffic volumes. In the current DRCOG model, 2001 daily traffic count data are available and coded at 900 locations. Classified count data are also available at about one-tenth of these locations. However, to provide sufficient traffic data for validating the estimated traffic volumes from the model (e.g., by time period and/or by vehicle type at screenline sites, cordon line sites and external stations), it may need to collect additional traffic count data to establish a base year traffic data inventory (database) for a sufficiently large number of road segments of the highway network. Additional truck traffic volume data also need to be collected to provide a better base to adjust the vehicle survey data for estimating the truck trip and external trip tables (to be discussed later). Also, in order to develop a model explicitly considering HOV/Toll traffic and non-motorized traffic, additional traffic data need to be collected at HOV/toll facilities and pedestrian/bike facilities.

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Transit Service and Patronage Data

Detailed transit service data at route level are available from RTD, including routing, schedule, and route operation times. These data provide the base to update the transit network of the model. RTD can also provide patronage data at route level. RTD also collects some P&R data, such as number of spaces, usage, license plate parked vehicles, for about 50 P&R lots. Passenger boardings and lightings and walk access distance to all bus stops and to some LRT stations are also available from RTD. Downtown Denver Parking Inventory and Cost Data

DRCOG collected detailed parking supply and parking cost data periodically (with the latest collected in 2001), including the type, number of spaces and parking fees charged at individual parking facilities in the central business area. This dataset is used to develop and update the parking cost model. Land Use and Demographic Data

DRCOG produces annual estimates and five-year interval forecasts (through 2020) of households and population, at traffic analysis zone level. The population data are also estimated and forecasted by age group at county level. DRCOG also produces employee estimates by employment type at TAZ level in five-year intervals. These data are supplemented by the Census survey data to develop finer level of segmentation (e.g., by household size and by car-ownership status, etc.). DRCOG also collects other related sources to support the household and employment data analysis, such as ES202 data, or customer data from the power companies. Limited school enrollment data are collected by DRCOG currently, since they are not used in the current model. With the development of a dynamic land use model, more detailed land use and demographic and business data can be provided from the land use model, e.g., data at sub-zonal (parcel) level, household data segmented by household size/composition and/or housing types, employment by industry, etc. This will significantly enhance the quality and the level of detail of land use and demographic data available for the development and application of the regional travel demand model. Freight Flow Data

A major source of freight flow data is the commercial database “TRANSEARCH”, which is a unified, multi-modal goods movement database designed for freight planning. It contains freight shipment data, such as origin, destination, volume and transportation mode, collected from freight carriers and public agencies. The origin and destination data are defined at county, zip code, metropolitan area and state level. Goods are defined by commodity type or Standard Industrial Code (SIC). Flow volumes are defined in terms of loads, tonnages, or values. The database contains annual data back to 1980. It can be used to analyze the long-distance commodity flow pattern at the regional or national level, (e.g., the IE or EE flow for a regional model). However, it lacks adequate data for analyzing intra-regional (within a metropolitan area) flow movements, for which a substantial amount of freight is carried by non-common carriers (privately operated trucks) whose data are not included in the TRANSEARCH database. In the Portland Regional model, the TRANSEARCH database was used to develop a strategic-level commodity flow model.

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Survey Data

A number of surveys conducted in the past several years can be used for the development of the regional travel demand model. Table 6.1.2 provides a summary of these surveys. Table 6.1.2. Survey Data Catalog

Survey Year Size Collection Method

Household Travel Survey 1997 4,196 households Telephone interview

Transit On-Board Survey 1997 677 transit user households

On-board recruiting followed by telephone interview

Commercial Vehicle Survey 1998-1999

3,681 vehicles in 4,903 businesses

Mail out survey form, followed by phone interview

External Stations Travel Survey 1998 6,700 vehicles Intercept/interview Air Passenger & Employee Travel Survey Data

1995, 1996 Unknown Unknown

Downtown Denver Partnership’s Employment Survey 2001 1,032 employees in

229 establishments Unknown

Downtown Parking Inventory and Cost Data 2001 All parking facilities Site survey

City of Boulder Year Transportation Survey

1997, 1998, 1999

About 400 households Phone interview

2000 Census Journey-to-Work Data 2000 16% (1 in 16

households) Mail out survey forms

2000 Census PUMS Data 2000 1% & 5% sample households Mail out survey forms

National Personal Transportation Survey

2001 (latest)

26,000 households nationwide Telephone interview

Traffic Speed Survey 2002 140 road segments Floating car with GPS data

Denver Regional Household Travel Survey Data

As part of the Denver Regional Travel Behavior Inventory (TBI), a household interview survey (HIS) was conducted in the Denver metropolitan region in fall of 1997, covering the following eight counties: Adams, Arapahoe, Boulder, Clear Creek, Denver, Douglas, Gilpin, and Jefferson. The 1997 HIS collected household and travel information from 4,196 households (about 0.5% sample size). In addition, 677 households with at least one household member taking the RTD transit service on the survey day were recruited and surveyed. The HIS in 1997 was a typical activity-based travel survey. The data collected in the survey are sufficiently detailed for the development of a conventional trip-based model. The data also provide trip chaining (tour) information as well as detailed household composition information. Such information allows for the development of tour-based model. Like most of household survey, the HIS did not collect sufficiently detailed toll use information (e.g., toll charge and toll facility used) that can be used to identify toll facility users. Nor it contains adequate data of non-motorized travel. Such information may be important for a mode choice model considering toll use and non-motorized modes as explicit travel modes.

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The survey was expanded in two steps. In the first step, the survey data were adjusted to match the survey estimated employed persons with the regional employment by home county and work county. In the second step, the data were further adjusted and expanded to match the number of households by county and by income group. The data were calibrated based on the controlled estimates derived from the 1990 Census PUMS data. The control dataset may be outdated as the study region underwent major development in the last decade. Thus it may be necessary to use the latest survey data (like Census 2000 data) to re-validate the travel survey data and possibly refine the adjustment and expansion factors of the 1997 HIS data. For example, it may be possible to compare the means and marginal and joint distributions of some household characteristics (such as household size, household income, auto ownership and number of workers) with the estimates from Census 2000 data. On-Board Transit Survey

Instead of a traditional transit on-board origin-destination survey, households with at least one member who uses transit were identified and surveyed as part of the HIS. The survey selected 677 households from 51 transit routes (out of 161 in the area), covering the entire survey region and all types of transit services (except the Mall Shuttle). The transit survey data provide the same travel data as those from the household travel survey, allowing for merging these two sets of data easily for developing a multi-modal regional model. The transit survey data also provide trip-chaining information for the transit-user households. A limitation of the survey data is the lack of information on the boarding and alighting locations, which may be used for transit access analysis. This type of information would be important for the travel analysis of premium transit services (such as rail and commuting bus services). It should be noted that the data were collected from all members of households, not just the transit-user member. It is unclear from the available reports how or if the transit survey data were weighted and expanded to reasonably represent the population of transit users (e.g., numbers of transit users by service type, etc.) in the region. More importantly, it is unlikely that the transit over-sampling within the home interview survey has yielded a sufficiently large and unbiased sample of transit users. This shortcoming could seriously affect the development of the mode choice model, as well as the transit network skimming and assignment components for a regional multi-modal model. Denver Regional Commercial Vehicle Survey

A commercial vehicle survey was conducted in 1998, as part of the Denver Regional Travel Behavior Inventory. The survey collected business and vehicle information from 4,903 businesses in the 9-county region (5.4% sample size). This survey was primarily intended to support the development of a commercial vehicle model. The survey was conducted in two stages. The first stage collected business and vehicle information from 4,905 businesses (representing 5.4% sample size) from telephone interview. The sample was drawn from the DRCOG business file. Businesses were stratified based on business size in terms of number of employees. The business survey collected characteristics of 3,681 vehicles garaged at business sites. In the second stage, the travel and activity information were collected from a stratified sample of 832 vehicles, selected from the vehicle pool established in the first stage. Vehicles were stratified based on business type (by SIC code) and vehicle type. The travel diary forms were mailed to the vehicle managers of the selected businesses to collect vehicle travel data.

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The survey data were expanded and adjusted through a series of factors. In the first stage, the business/vehicle data were expanded with two sets of expansion factors:

1. Business expansion factors to expand the sampled business data to represent the universe of all businesses considered in the survey, by business size, and,

2. Vehicle expansion factors to expand the inventory vehicles to represent the total number of vehicles garaged at the survey universe of business, by business size group and numbers of vehicles owned/operated (considering businesses with more vehicles likely to under-report vehicles operated).

In the second stage, the vehicle travel data were expanded to represent all vehicles in the inventory. The expansion factors were classified based on business size, number of vehicles and vehicle type and business type (a total of 21 expansion groups). In addition to the above two sets of the expansion factors, a number of adjustments were also made to the survey data, to consider the following:

• Businesses excluded in the survey (e.g., truck and passenger car rental, Postal Service, utility trailer rental, refuse systems, etc.),

• Self-employed persons that own and operate commercial vehicles, and • Vehicles routinely driven home by employees or drivers.

In order to evaluate how well the commercial vehicle survey data represent the regional commercial vehicle travel, the survey also developed alternative regional estimates of number of commercial vehicles and vehicle miles of travel, and compared with the estimates from the survey data. Basically, the commercial vehicle survey collected valuable information regarding travel characteristics of commercial vehicles, such as trip length, trip purpose, number of trips made per day by various types per vehicle, type of vehicles used, etc. However, a number of issues regarding the reliability of commercial data for model development need to be addressed:

• There is no reliable or well-recognized control total regarding the number of commercial vehicles routinely operated in the region.

• The survey only accounted for about half of the roughly estimated total number of commercial vehicles operated in the region.

• The vehicle travel data represent only about 1% (5.4% business sample times 22.6% of vehicle sample from the business sample) of all vehicles of the businesses considered in the survey, which covers a large variation of business type, business size and vehicle type.

• The survey data were expanded and adjusted through a series of factors based on a number of factors like business type, business size, vehicles operated, and vehicle types. The sample sizes for some of the groups are of concern.

• There was limited consideration during the data sampling, calibration and adjustment stages to reflect the geographical distribution of commercial vehicle travel.

The data of large truck travel may be a major concern as truck traffic is particularly important for certain highway facilities. There were only 300 trucks surveyed and 1,600 records of truck trip data collected in the survey. It is unlikely that the 1,600 trip records can provide sufficient data to develop a commercial vehicle (truck) model reliably enough to describe the truck travel pattern (in terms of amount of travel, geographical distribution, time of day, etc.) in the region.

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With the issues of the commercial vehicle survey data as mentioned above, it is believed additional surveys or traffic data need to be conducted or collected to complement the commercial vehicle survey data, or to provide information to correct the potential bias of survey data, including:

• Extensive classified traffic counts to validate and adjust the survey data or the commercial vehicle model developed from the survey data, and

• Special truck OD surveys at number of road segments with major truck traffic. External Stations Travel Survey

An External Travel Survey was conducted in the spring of 1998, as part of the TBI. It was a roadside interview survey in which over 9,600 motor vehicles (6.8% sampling rate) passing 9 external locations (5 Interstate locations and 4 non-Interstate locations) were surveyed. Survey forms were distributed to small vehicle drivers and large truck drivers to collect information regarding the vehicle (vehicle type, fuel type, etc.) and the trip (origin and destination, occupancy, trip purpose, etc.). These data can be used to derive a set of external trip tables (e.g., by vehicle type, by time period, etc.), or some kind of frequency distribution describing the geographical distribution pattern of the external travel. It is believed that data collected from the small vehicle survey are adequate for deriving a set of reasonable trip tables at sub-regional (super-district) level. However, the survey may not provide data reliable enough for deriving truck trip tables, because of the following concerns:

• Losing samples for the single-unit trucks with gross vehicle weight less than 26,000 pounds on the Interstate sites.

• The survey period (13-hour) covered only 60-70 percent of the daily truck traffic at the external locations. It may not be representative of daily truck traffic as many long-haul truck drivers travel during the late night hours to avoid traffic in metropolitan areas, and hence generating significantly different travel characteristics from those of day-time truck traffic.

Additional data thus need to be collected to correct the potential bias from the above two problems. Denver International Airport Access Survey

Shortly after the Denver International Airport (DIA) was opened in 1995, several surveys were conducted to collect ground access travel data of passengers and employees to the airport, including:

• Origin and Destination Survey for Air Passengers at DIA, December 1995 • DIA Employee Travel Survey, December 1996 • Survey of Commercial Ground Access Service Companies, 1996

The first two surveys provide sufficiently detailed information for analyzing personal travel characteristics to the airport, such as trip distribution, access mode, trip purpose, time-of-day, etc. The commercial ground access services survey collected operational data (such as fleet size, operation frequency, fare schedule, etc.) of various passenger transportation operators providing services to the airports, including airport shuttles, hotel/motel courtesy vans, charter buses, mountain carries, taxis, etc. A potential shortcoming of these data sets is that they may be dated, since the surveys were conducted almost a decade ago, shortly after the opening of the airport. The travel characteristics of air passengers and airport employees may have changed significantly since the opening of the airport, due to events such as:

• Change of operation of air transportation, e.g., low-fare airlines, security measures, etc.

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• Change of ground access transportation facilities, e.g., transit services, spaces and airport parking fees, etc.

• Development of areas with close travel interaction with the airport, e.g., major commercial and industrial centers along travel corridors near the airport.

The airport access travel data thus may need to be updated. Downtown Denver Partnership Workforce Survey

A downtown employee survey was conducted in 2001 to collect the socio-economic and travel information of employees in downtown Denver area (including Lodo, Central Downtown and Golden Triangle areas), as well as information regarding their attitude towards moving to the downtown area. The survey collected information from a sample of 1,032 employees at 229 establishments (out of 113,000 workers in 4,500 firms in the survey area). It provides valuable data for analyzing the socio-economic and travel characteristics of downtown employees, including:

• Residential location • Household size and household composition • Income level • Age and gender • Occupancy • Travel mode used for commuting • Commuting time

This dataset can be used to supplement the household survey data and the census data for development the destination choice and mode choice elements of the model, e.g., the development of area-type related variables (measures) for the models. City of Boulder Annual Transportation Survey

The City of Boulder conducted annual surveys from 1997 to 1999 to collect information from about 4,000 city residents about their perceptions and opinions regarding the transportation system in the city. The major purpose of survey is to track trends in resident’s general satisfaction, perceptions, and behavior related to transportation system. The survey collected mostly qualitative information in rated or scaled measures. It does not provide much quantifiable data for model development. However, it may help in identifying particular transportation issues and factors that could be considered in the travel model, such as residents’ perception on the use of walk paths or bike paths. Census 2000 Data Journey-to-Work (JTW) Data

The Census 2000 Journey-to-Work data, as packaged in the Census Transportation Planning Package (CTPP) is another major source of travel demand analysis. The dataset contains tabulations at various geographical levels of the following three parts of data:

1. Place of residence data 2. Place of work data 3. Worker-flow data

The data were collected from questionnaires mailed to one in six U.S. households during the 2000 Decennial Census Survey. The database can be used in various travel analyses, such as updating travel demand models and segmenting demographic and employment data. However, it should be noted that the Census survey collects travel data of workers on a typical weekday the week before the survey, while most of household surveys collect travel data of a specific weekday. Also, the survey only collects work trip information. The above issues limit the use of the Census JTW data for the development of a regional

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model for a major metropolitan area. However, it can be used to supplement the household survey for specific markets with limited household survey data. Census 2000 Public Use Micro-Data Sample (PUMS)

The Census Public Use Micro-data Sample (PUMS) data is a subset of the Census data records. The 1-percent PUMS is a 1-in-100 sample of the census “long-form” surveyed households, while the 5-percent PUMS is a 5-in-100 household sample. These data records can be considered as the disaggregate raw data of the Census Survey. The PUMS datasets are released at geographical areas called Public Use Microdata Areas (PUMAs). The 5% PUMS data are available for PUMAs that have more than 100,000 people, while the 1% PUMS data are available for “super-PUMAs” with at least 400,000 people. The PUMS datasets contains rich information about household characteristics, and are valuable data sources for transportation planning applications. Typical use the PUMS for travel demand modeling includes:

• Synthetic population micro-simulation for tour-based modeling • Discrete choice modeling of household vehicle and worker level • Weighting and expansion of household travel surveys • Descriptive statistics of various household segments

The Census 2000 PUMS datasets are now available and will be major source data for updating the regional model. National Personal Transportation Survey (NHTS)

The National Household Travel Survey (NHTS) is the nation’s inventory of daily and long-distance travel of sampled households. The surveys were conducted in 1969, 1977, 1983, 1990, 1995 and 2001. The NHTS, conducted using Computer-Assisted Telephone Interviewing Technology, collected travel data from a national sample of civilian, non-institutionalized population (excluding people living in college dormitories, military bases, etc.) In the 2001 NHTS there were about 26,000 households in the national sample. The NHTS basically provides travel data at the national level (except for nine regions requesting explicit samples for their regions). Thus the use of this dataset for the development of a regional travel demand model is limited. Traffic Speed Study

A traffic speed study was conducted in 2002 to collect travel time and speed data of about 140 road segments, using floating car method with GPS equipment. The survey not only provides travel time data of individual segment, but also minute-to-minute location information that can help to analyze travel speed and delay at a very detailed level. This dataset is sufficient to develop travel speed data, such as speed lookup table and volume-delay functions for the model.



6.2. General Assessment of Available Data In this section, an overall assessment of the available data cataloged in the previous section is presented to examine their adequacy and effectiveness for the development of a state-of-practice regional travel demand, either trip-based or tour-based model. The discussion covers four aspects: level of segmentation, tour-level analysis, trip-level analysis, and network analysis.

Level of Segmentation

The existing travel data are at the level of detail enough for the travel segmentation required for the development of the trip-based model and tour-based models. For example, the definitions of travel purpose, travel mode, and travel participation in the HIS are fine enough, and the sample size is large enough, to develop the various models for either a trip-based model or a tour-based model. Detailed household and employment data with multi-dimensional segmentation can be developed with the proposed land use model or from the Census PUMS data. The current DRCOG model has a zone system with about 2,600 traffic zones. The zone structure is fine enough for a state-of-practice regional model.

Tour Level Analysis

The data from the household interview survey provide most of the necessary information for the tour level analysis, such as the construction of tours and journey with detailed information regarding main tour purpose, activity locations, modes used, as well as departure and arrival time. Also, the HIS collected detailed data regarding the socio-economic and composition characteristics of the households, which is necessary to develop household submodels. These data may also be complemented with Census (including PUMS) data to ensure that the various marginal and joint distributions are representative of the region, as well as to supplement the HIS observed OD patterns. The HIS data can be used to develop tour-level model elements (such as tour generation, tour sequencing, main mode choice, etc.) at a level of detail comparable to that used in state-of-practice tour-based models such as the Columbus model.

Trip Level Analysis

The 1997/1998 Travel Behavior Inventory data can be used to develop most of the trip-level model elements for a state-of-practice trip-based model or tour-based model. However, as mentioned before, the HIS data may need to be re-expanded using the latest Census 2000 data. The HIS data can be supplemented with data from other special surveys to handle the specific travel characteristics of certain markets, such as the Downtown Denver Employee Survey, Denver Airport Employee and Visitor Survey. There are a few issues regarding the household travel modeling that need to be addressed:

1. Transit user data: The transit user data collected from the transit over-sampling household survey are not adequate for the development of a state-of-practice mode choice model and transit network model. The lack of a transit on-board survey data set will lead to serious problems in:

• Mode choice model estimation • Path parameter creation • Calibration target value computation • Validation of transit assignment results

2. Toll/HOV options: The HIS data do not explicitly separate the auto users that used Toll/HOV

facilities from other auto users (although the HIS did collect vehicle occupancy and travel cost data). This may be an issue for the development of a mode choice mode that explicitly considers



Toll/HOV as travel modes. Additional surveys collecting the travel and household characteristics of the Toll/HOV facility users may be necessary. Also, more detailed operational toll data need to be collected and supplied to the mode choice model, such as the actual and perceived toll paid by various types of traveler (e.g., SOV, HOV2 and HOV3 vehicles) at various time periods, expected delays at toll plaza, etc. These will require the enhancement of the model highway network.

3. Non-motorized modes: The HIS did consider non-motorized modes explicitly and the survey data

show non-motorized modes account for significant shares of total travel. However, there may not be enough data records to analyze the travel characteristics of non-motorized users. Also, there is no “supply” data about non-motorized facilities (e.g., walk/bike path, etc.) from the network. This type of information needs to be included in the highway network. In areas with high pedestrian volumes, walk networks can be developed, and special pedestrian travel survey can be conducted to collect additional travel data for pedestrians.

4. Destination end data: To support the development of an advance trip distribution model (such as

destination choice model or stratified gravity model), more detailed destination end data needs to be developed, including employee data segmented by income or car-ownership group, school enrollment by grade group, etc. It is believed that the land use model to be developed will provide more detailed destination end data that would be adequate for travel demand modeling. There are also several sources of data providing employee data, including the Census CTPP Place of Work Dataset, the Denver Downtown Employee Survey data, and the Denver Airport Employee and Visitor Travel Survey data. For other major employment centers with specific employment characteristics (e.g., major research centers, universities, etc.), special surveys may be needed to collect employment data (as part of special generator surveys).

5. Special Generator Survey Data: Except for the Denver International Airport, and the community

college campus in downtown area, it is not known if there are any data for other special generators. The current DRCOG model only considers the above two special generators. Some employment generators may have significantly different travel characteristics, such the Downtown Technology Center, the warehouse/distribution area in the Denver NE area, etc. The travel characteristics of these special generators need to be collected and investigated.

For commercial vehicle modeling, the truck from the 1998 surveys may not be reliable because of the small sample size of truck trips in the commercial vehicle survey and the problem of missing single-unit truck data in the external station survey. It is unlikely that the commercial vehicle survey and external station survey provide sufficiently reliable truck travel data that allow for estimating a set of reliable district-to-district truck trip tables. Additional surveys thus need to be collected, such as classified traffic counts, Truck OD survey at locations with high truck volumes.

Network Analysis

As mentioned before, the highway network needs to be enhanced to include more detailed operational and toll related characteristics of the roadway facilities, such as the following:

• Existence of HOV, Toll, and bus lane facilities • Availability of Bike/walk path facilities (or a non-motorized network) • Toll level for various types of users/vehicles • More detailed facility categories to reflect the differences in operational characteristics • More detailed intersection and interchange coding. • Delays at crossings and toll plazas • Ramp metering



• Signal control data (% green time) • Roadside development attribute (driveway density or signal density) • Roadside parking

Most of the data mentioned above are available from existing data sources. The major effort will be to add the link attributes into the network, and verify and update the attribute data routinely. The existing highway network was converted from the MinUTP platform to the TransCAD platform. It is not a true GIS-based network (e.g., without shape points). In the next generation model, a true GIS-based highway can be developed. A GIS-based highway network not only enhances the presentation function of model data, it also allows for conducting geographical analysis and developing more detailed service measurements for the regional model, such as sub-zonal level walk accessibility measures. DRCOG maintains a base roadway network layer that can serve as the base for the development of the GIS-based network. The new generation model will be at a finer level of detail than the current one, in terms of time period, facility type, and travel mode. More detailed traffic data thus are needed to validate the model. Additional traffic data will therefore need to be collected, including:

• Traffic count data by time of day • Classified count at high truck volume locations • HOV and Toll traffic counts at toll facilities • Pedestrian volume counts • Speed and delay data of specific facilities (e.g., toll links, toll plazas, etc.)

Additional information may also need to be coded in the transit network. Most of the data listed below is already available to the RTD:

• More detailed operational data for P&R lots (size, distance to stations, parking charge if relevant) • Demand characteristics of P&R lots (% occupied, residence locations of users, etc.)



6.3. Data Assessment to Match to Model Structural Improvements

Trip-Based Model Improvements

More Disaggregated Travel Decisions

The household survey collected household and travel data at a very fine level of detail that can support more disaggregate segmentation in trip purpose, household type (e.g., car-ownership, income group, etc.) and time of day (e.g., 4 time-of-day periods). In addition, the Census 2000 PUMS data can be used to complement the household survey data to develop the marginal and joint distributions of household types for more detailed household-based segmentation. Additional Consideration of Household Characteristics

The household travel survey and Census 2000 survey can provide most of data required for the development of a car-ownership and workers sub-model. The sample size of household survey data is adequately large for model segmentation at the level that is comparable to the state-of-practice trip based models (such as 3-5 non-work trip purposes and 2+ non-home based purpose, 3-5 income groups, and 3-4 car-ownership levels. Destination Choice Model

The household survey data provide sufficient data for the development of the destination choice model segmented by trip purpose and by household type (e.g., car-ownership group). The downtown employee survey data also provide information that may be used to derive downtown specific variables. Additional employment data may need to be collected to allow for the stratification of employment data by car-ownership or income level, in particular for major employment centers with specific employment characteristics. The highway network and transit network are detailed enough to develop logsums (from the mode choice model) and other zonal level accessibility measures. The GIS base network layer can be used to develop sub-zonal measures. Some land use variables (e.g., mix land use pattern and development density measure, etc.) can also been derived with the use of the GIS data. Comprehensive Mode Choice Model

Household travel data (with the augmented transit users’ survey data) are adequate to consider transit options at transit sub-mode and access mode level. However, additional travel data on HOV, toll and non-motorized users need to be collected in order to provide adequate data for these new modes to be considered in the mode choice model. The operational data of HOV, toll and non-motorized facilities also need to be enhanced in the model network. The travel cost data also need to be enhanced as follows:

• Transit pass discount • Discounts with electronic toll passes • Parking cost, availability of free parking • Parking cost at P&R lots (if relevant for future year analysis)

Capacity-Restrained Transit Assignment

The transit network is adequate for development of a capacity-restrained transit assignment module, although it will require coding vehicle capacities on the network. The additional data required for this module are parking supply and average daily demand information at P&R lots (demand data would be required for calibration and validation purposes). All of these data are available from the RTD.



Traffic Volumes

As mentioned before, additional traffic data may be needed to validate the model, including:

• Traffic count data by time period at all major screenline, cordon line and external stations • Classified count at high truck volume locations • HOV and Toll traffic counts at toll facilities and HOV lanes • Pedestrian volume counts • Speed and delay data of specific facilities (e.g., toll links, toll plazas, etc.)

Commodity Flow Model

The TranSearch dataset can provide certain data for the development of a commodity flow model, in particular for the long-haul inter-regional flow (Internal/external flow). The commercial vehicle survey can be used to model internal flow. However, additional data may need to be collected at major commodity distribution centers and major warehouse/industry parks. Also, additional truck volume data and truck OD data need to be collected to support the calibration and validation of the commodity flow model. IE and EE models

The external travel survey conducted in 1998 is the major source of data. However, additional truck volume data may need to be collected to validate the external travel survey data and correct the possible bias of the truck data as mentioned before.

Tour-Based Model Improvements

The data requirements for a tour-based model are basically similar to that of an advanced trip-based model. The various tour-based models reviewed in this study (Columbus, San Francisco and Portland) were developed using datasets similar to those used for trip-based models. Also, many of the structural improvements suggested for a trip-based model also apply to the tour-based model with similar data requirements. The major difference related to the use of travel data between the tour-based model and trip-based model is that the tour-based model represents the household characteristic and the resulting travel pattern in more detail than the trip-based model, in particular in the following three aspects:

• More detailed description of household characteristics (such as household lifecycle, household composition in terms of numbers of full-time and part-time workers, numbers of children by school grade or age group, etc.)

• Finer classification of tour type (e.g., separated joint tour and individual tour types, for example) • More rigorous consideration of intra-household interaction of travel decisions and the resulting

joint travel activities of the household

The above differences between these two types of models do not necessarily imply that the tour model requires more detailed data or larger sample size of travel data than the trip-based model. Instead it reflects the more effective use of travel data in the tour-based model. The household travel survey contains very detailed household characteristic data (such as age and employment status of each household member, car-ownership, etc.). In addition, the Census PUMS data can complement the household survey data to develop marginal and joint distributions for multi-dimensional segmentation of households, and can be used to develop various “uncontrolled” household variables (such as full-time & part-time workers, students by age group, household income, etc) for various household types.



The household travel survey also provides sufficiently detailed tour data (in terms of tour types, length of activity, main tour mode and trip model) that can allow for the development of various tour-level model elements (like tour generation, stop frequency and stop location analyses). Furthermore, the implementation of the state-of-practice tour-based models with the synthetic population simulation technique actually requires smaller amount of household and travel data for model development and application than advanced trip-based models that apply large numbers of market segments.



6.4. Recommended Data Collection Efforts DRCOG already collects and maintains a large amount of data that can be used in the development of a state-of-practice regional travel demand model. Based on the above analysis, a few further surveys are recommended to collect additional data to support the model development effort. Table 6.4.1 presents a list of these recommended surveys, which basically serve the following three functions:

1. To support the new model that explicitly represents new or low share travel options (such as users of toll/HOV facilities and non-motorized modes),

2. To support the validation of the model which is expected to be at a finer level of detail than the existing one, and

3. To enhance data of specific types of travel (such as commercial vehicle travel, external travel, and travel to special generators).

Table 6.4.1. Recommended Additional Surveys

Survey Rationale Size Collection

Method Estimated

Cost High Priority

Transit On-board Survey

Transit user data for mode/sub-mode choice model, and transit path building parameters

9,000 to 10,000 transit trip records

Survey forms distributed to transit riders

$300K*

HOV and Toll Users Survey

Travel data HOV/ toll users for considering HOV and toll options in the model

1,000 vehicles from 3-5 HOV/Toll facilities

Post card survey $75K

Medium Priority

Pedestrian/Biker Survey

Non-motorized Travel data for considering non-motorized modes in the model

2,000 samples from 3-5 locations

Intercept-post card survey

$50K-$60K

Traffic Count Studies

Model validation and calibrate/adjust commercial vehicle survey and external station survey

Classified counts for 3-4 screenline / cordon lines; plus other locations with large truck traffic

Automatic Counter $20K-$40K

Low Priority

Special Generator Surveys

Travel data at major universities, employment centers and distribution centers

Colleges/University, other major employment center with specific travel characteristics

Intercept survey or data mining from records

$15K/site

Truck OD Survey Update

Supplement 1998 Commercial Vehicle Survey and External Station Survey data

7-10 locations with high truck volumes

Roadside intercept- interview survey

$100K

* Sample size and cost estimated based on 160 routes to survey. A more detailed description of each of these data collection activities follows.



Transit On-Board Survey

The transit on-board survey will collect detailed travel data of transit users, and will supplement the household survey data in the development of a state-of-practice mode choice model. The on-board survey data can also be used to calibrate transit path building parameters, accomplished by assigning observed trip tables to the network and validating the assignment results against boardings. The survey will collect information on a trip basis, including origin, destination, purpose, time, fare, etc., as well as the rider’s socio-economic information similar to that of the household survey. It is important that all transit routes be surveyed and that the survey sample is unbiased with respect to geography, time of day and type of service. The survey will be conducted by handing a survey form with a specific series number to each passenger boarding the transit vehicle being surveyed. The estimated cost and sample size are based on the need to survey 160 routes.

HOV/Toll Users Survey

The HOV/Toll users’ survey is intended to collect travel information of people who use toll/HOV facilities, which will become more important in the future. The 1998 household survey captured only limited information about these users. The survey can be conducted by distributing survey forms to the drivers at specific locations (e.g., toll plazas, etc.). The data of non-toll/HOV travelers should also be collected, in order to investigate of differences in travel characteristics between the two groups of people at the same road segments (or comparable routes). The survey forms of HOV and SOV lanes users can be distributed in two steps. In the first step, a sample of vehicle license numbers is recorded (manually or through video-recording), and in the second step, the survey forms are mailed to the owners of the identified vehicles. The survey forms should collect information of the socio-economic data of the travelers in the vehicles (like household size, income level, number of vehicle owned, etc.) and travel information (such as origin/destination, main purpose, occupancy, number of places stopped and stop purpose, etc.)

Non-motorized Travel Survey

The non-motorized travel survey is intended to collect information of pedestrian and bikers. The non-motorized mode will be explicitly considered in the mode choice model, but the household survey data contains limited data records of non-motorized travelers. The survey can be conducted by intercepting pedestrian or biker and distributing survey cards or interviewing them directly, at locations/facilities with high-non-motorized traffic volumes, such as downtown walk paths, bicycle parking facilities. The survey should collect information about the socio-economic characteristics of travelers (such as age, household size, household income, etc.) and travel information (such as origin/destination, travel purpose, etc.)

Traffic Count Studies

Extensive traffic count data are needed to establish a base year traffic volume dataset to validate the traffic assignment results of the model, and on some occasions to provide the basis to adjust the model. Count data should be available at all external stations, along screenlines and cordon lines, and other locations with significant traffic. Additional classified counts are also needed at locations with high truck volumes. Most of the count data can be provided by the CDOT traffic count program or from CDOT traffic count databases. Existing count data that are 1-2 years old may be usable if they can be adjusted to the base year volumes. However, it is expected that additional counts are needed to supplement existing count data, e.g., traffic count data by time period. Also, for locations with high pedestrian or non-motorized volumes, pedestrian or bike counts should also be conducted.



Special Generator Surveys

These surveys seek to quantify the trip generation, distribution and mode choice characteristics of trips attracted to establishments that are unique in the region, either due to their size or the nature of the economic activity that takes place in their facilities. These trip characteristics are typically not adequately captured by the household survey or by other standard data sources. These establishments include major universities, major employment centers, and major recreational facilities, including natural areas such as parks. Where feasible, the special generator surveys should go beyond simple counting of driveway volumes. People coming to the facilities (employees and visitors) should be surveyed to obtain trip purpose information, vehicle mode and occupancy, and origin/destination information.

Truck OD Survey

To supplement the truck travel data collected in the 1998 Commercial Vehicle Survey and the External Station Travel Survey, an additional truck OD survey may need to be conducted. This survey can be conducted by intercepting a sample of trucks and interviewing truck drivers at locations with high truck traffic volumes (similar to the 1998 External Station Travel Survey). Information to be collected in the survey should include: vehicle information (such as vehicle type, fuel use, etc.), travel information (including origin/destination, purpose of last and next stops, commodity types, etc.).


SECTION 7

TOUR-BASED MODEL DEVELOPMENT

COST ESTIMATE


7. Tour-Based Model Development Cost Estimate

This final section provides an estimate of the development cost (expressed as level-of-effort) of an advanced tour-based / activity-based / micro-simulation model system for DRCOG. The level of effort estimation presented in here is primarily based on the growing experience of PB Consult in the development and application of advanced model systems in several metropolitan areas, including San Francisco County Transportation Authority (SFCTA), New York Metropolitan Transportation Council (NYMTC), Mid-Ohio Regional Planning Commission (MORPC), and the Atlanta Regional Commission (ARC). The details underlying the level of effort associated with various steps of model development are based on a realistic estimation of time required to implement each step in conjunction with the required technical qualifications of the project participants. The number of person hours of effort described below corresponds to the average required time scaled to the size and complexity of the DRCOG region and regional transportation network. Each identified staff position can, or course, be split or shared between several persons of the same qualification, either within the consultant team or among the consultants and DRCOG staff. This memorandum presents a generalized estimation for a model development project based upon the recommended model specifications developed for this project. A realistic time framework for this project will be between one and two years depending on the concentration of efforts, availability and suitability of data, and availability of budget. The level of effort estimation is presented at two different levels of details:

• Level of 10 major tasks • Level of sub-tasks for each task

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7.1. Cost Estimation at the Major Task Level The development of an advanced regional travel demand model system is a complex and challenging project that is associated with numerous tasks. These tasks logically fall into 10 major categories:

• Administration • Workshops / Presentations / Outreach • Core Travel Demand Model Development and Estimation • Ancillary Model Development and Estimation • Network Processing Procedures • Population and Land Use Data Preparation • Application Programming • Calibration and Sensitivity Analysis • Final Technical Report and User Manual • Technical Support and Client Training

In general, formation of the model development team and estimation of the associated level of effort can be quite flexible depending upon available staff resources and the extent to which DRCOG can contribute person hours of effort. Also, the amount of hours that is necessary to implement each task is a strong function of the model developers’ qualifications and experience with this particular type of model / software components. The growing experience of PB Consult with tour-based activity-based micro-simulation models provides the foundation for an outline of fundamental staffing principles and requirements that overall create a realistic framework for the project costing. This framework is based on the assignment of tasks listed above to specific staff position descriptions. For purposes of developing a level of effort, six typical staff positions are necessary to effectively cover all model development tasks:

• Senior project manager / Quality control person. The tour-based model development represents a complicated technical project accumulating the state of the best practice and in many respects bordering the research field. Experience has shown that model development project of this scale is associated with a great interest from the broad community of transportation planners and modelers on a national and sometimes international scale. There are numerous quality control measures associated with this project including coordination with the DRCOG staff and other interested local agencies, a national peer-review committee, outreach efforts at national and international forums. For these reasons a top level professional with strong credentials in both practical and academic communities as well as with outstanding communication skills should lead the project.

• Senior modeler. Tour-based model development is associated with a complex model design and estimation procedures that require prior experience with this type of model development. The structure of the model system is based on contemporary activity-based platform that requires a deep knowledge of various models including choice structures, duration models, structural equation systems, entropy-based models, etc. The person who takes a technical lead on the model system design and statistical estimation should be a top level professional with a strong theoretical background and practical experience with model design and estimation.

• Senior application programmer. To the same extent as the tour-based model is different from conventional models with respect to the design and estimation complexity, it is different from the application programming point of view. The model system implementation is associated with a complicated chain of sub-models and numerous sub-

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routines. Effective implementation of this system in terms of realistic run time and convenient user interface requires special software architecture. This in turn requires a top level programmer with professional skills and full proficiency with the chosen language as well as familiarity with the model structures.

• Modeler. This position is reserved for a large proportion of the technical work on the model estimation, data preparation, utility expression coding, etc. This person works under the supervision of the senior modeler and takes most of the standard sub-tasks (like for example, running the ALOGIT software for the choice model estimation). This position requires familiarity with the mode structures as well as proficiency with the special software for model estimation.

• Programmer. This position is reserved for most of the technical work on the application programming side. This person works under the supervision of the senior application programmer and takes most of the standard sub-tasks (like for example data transformation and preparation of the arrays of variables that are necessary for calculation of the choice utilities). This position requires proficiency with the chosen software platform.

• Technical support. This position is reserved for technical work including data processing and analysis, setting up and implementation of model runs, network analysis and processing, etc. This position requires only basic technical skills and experience with transportation models. Persons on this position work under supervision of modelers and programmers.

The major task assignment by staff positions and associated level of effort estimation is presented in Table 7.1.1 below. The total model development level of effort is just under 10,000 hours of labor. The task allocation to staff positions follows the rules of maximum efficiency and correspondence of the person qualification to the task. Table 7.1.1. Level of Effort Estimation at the Main Task Level

Hours by Staff Position Main Tasks Senior

PM/QC Senior

Modeler Senior

Programmer Modeler Programmer Technical Support Total

1. Administration 80 30 30 140 2. Workshops / Presentation / Outreach 94 202 202 30 10 60 598 3. Core Travel Model Development and Estimation 28 286 50 1,052 160 80 1,656 4. Ancillary Model Development and Estimation 27 158 416 360 961 5. Network Processing Procedures 25 110 400 340 875 6. Population and Land Use Data Preparation 10 40 140 210 400 7. Application Programming 30 310 750 320 1200 70 2,680 8. Calibration and Sensitivity Analysis 20 250 130 260 110 700 1,470 9. Final Technical Report and User Manual 48 120 56 320 120 80 744 10. Technical Support and Training 16 44 104 144 104 44 456 Total Hours by Staff Position 378 1,550 1,322 3,082 1,704 1,944 9,980

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The senior project manager / quality control expert has overall a limited number of hours (less than 5% of the total). His/her participation is mostly concentrated on the administration and presentation tasks (1 and 2) with also a quality control scattered over all other task with a limited number of hours (10-30 hours each). The senior modeler takes about 16% of hours. This person takes the lead on the core model development and estimation (task 3) and model calibration and sensitivity analysis (task 8) as well as is intensively involved in workshops and technical reports (tasks 2 and 9), specifications for application programming (task 7), and other tasks. The senior application programmer takes about 14% of available hours. This person takes the lead on the application programming (task 7) as well as intensively involved in the workshops / technical reports (task 2) and calibration effort (task 8). The modeler position takes about 29% of the labor hours. This person(s) is primarily involved in the core model estimation (task 3) as well as the other technical tasks (tasks 4-9). The programmer position takes about 17% of the labor hours. This person is primarily involved in the application programming (task 7) with additional involvement in the core model development and coding (task 3), calibration and sensitivity analysis (task 8), and other technical tasks. Technical support constitutes 19% of the labor hours. These hours are mostly needed for technical tasks 4-6 and 8.

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7.2. Cost Estimation at the Sub-Task Level

Task 1 - Administration

This task includes general administration within the modeling team as well as administrative aspects of the communication and coordination with DRCOG. This task is relatively modest in terms of the hours and cost compared to the most of the other tasks. However, experience has shown that lack of attention to this aspect of the project leads to an underestimation of the real project cost. It should be noted that a project of this scale is normally implemented by a group of 5-8 persons from the consultant side with the addition of another 5-10 persons from the client side and plus possibly sub-consultants. Key importance is placed on inter-person interactions, internal reporting, and staff coordination. Table 7.2.1. Administration Level of Effort

Hours by Staff Position Main Sub-Tasks Senior

PM/QC Senior

Modeler Senior


General administration, staff management 50 20 20 90

Relationship with client 30 10 10 50 Total Hours by Staff Position 80 30 30 140

Task 2 - Workshops, Presentations and Outreach

Experience has shown that successful implementation of the project requires intensive professional coordination with DRCOG and all interested regional agencies, and must be supported by detailed reporting of the intermediate steps and decisions made in the course of the project. The most effective form of professional coordination is a series of full-day workshops normally on a monthly or bi-monthly basis where presentations are made by the consultant team and/or local staff for a wide audience. Intermediate reporting and workshops facilitate the necessary constructive dialogue between the client and consultant, fosters the elimination of misunderstandings and possible errors in the model specification, and serves as a good basis for the final technical report. Table 7.2.2. Workshops, Presentations and Outreach Level of Effort


PM/QC Senior

Modeler Senior


Eight one-day workshops 64 64 64 192 Technical memos / presentations 128 128 30 10 60 356

Outreach efforts 30 10 10 50 Total Hours by Staff Position 94 202 202 30 10 60 598

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Task 3 - Core Travel Model Development and Estimation

This is one of the major task areas that require highly experienced modeling staff. In contrast to the conventional four-step model system, that has only a limited number of relatively independent sub-models to design and estimate, the advanced tour-based activity-based model system includes several sets of choice models that must be carefully designed and statistically estimated taking into account quite intricate interactions between them. Also, a micro-simulation framework allows for much more detailed segmentation of the choice models as well as introduction of virtually all relevant household and person variables available from PUMS. This improves the statistical quality of the models tremendously but requires much more work in terms of statistical analysis and estimation compared to the conventional models. The level of effort below assumes that 90% of the model structure will be borrowed from MORPC. Table 7.2.3. Core Travel Model Development and Estimation Level of Effort


PM/QC Senior

Modeler Senior


HIS transformation to tour-based format 16 80 80 176 Data processing (skims, land use, parking cost) 40 40 80 80 60 300 Population synthesizer – specifications/estimation 2 30 10 20 20 82 Usual workplace/school location and schedule 2 16 80 98

Car ownership model 2 8 40 50 Daily activity pattern type model 2 16 80 98 Fully joint tours generation / person participation 2 24 48 74 Partially join tours generation / person participation 2 24 64 90 Allocated maintenance activities generation / person allocation 2 16 40 58 Individual activities / person pattern details 2 24 80 106 Time of day choice model 2 16 80 98

Destination choice model 2 16 80 98 Entire-tour mode choice/ car allocation model 2 16 80 98 Secondary stop-frequency model 2 8 80 90

Trip mode choice model 2 8 80 90

Parking lot choice model 2 8 40 50 Total Hours by Staff Position 28 286 1,052 1,656

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Task 4 - Ancillary Model Development and Estimation

The regional model system includes several additional models that correspond to various travel and traffic components not covered by the core model. They include external trips made by the residents and non-residents of the region, freight components (trucks, commercial vehicles), taxi trips, and also possibly special trip generators (airports, major sport arenas, etc). All of these types of travel may be understood as non-household travel. There are also several auxiliary models like the parking cost model, parking capacity model, and person free parking eligibility model that provide inputs to the core choice models. It should be noted that the current project framework assumes only a reasonable aggregate level of implementation of ancillary models within a limited budget similar to the way they are implemented in the conventional four-step model. There is a growing body of research on freight transportation modeling including microscopic simulation models. However, implementation of a freight transportation model of the new generation represents a significant modeling effort in itself that requires a special budget that is not automatically included in this estimate. Table 7.2.4. Ancillary Model Development and Estimation Level of Effort


PM/QC Senior

Modeler Senior


External trips 5 40 100 100 245 Non-travel components (trucks, commercial vehicles, taxi) 5 40 100 100 245

Special generators 5 40 100 100 245

Parking cost model 5 10 50 20 85

Parking capacity model 5 20 50 20 95 Free parking eligibility model 2 8 16 20 46 Total Hours by Staff Position 27 158 416 360 961

Task 5 - Network Processing Procedures

This task is mostly technical; however, it is of great importance for the whole project since network processing procedures serve as a source for level-of-service skims as well as for network assignment and validation procedures. Experience in each of revious projects of tour-based activity-based model developments has shown that in many cases deficiencies and simplifications in the network processing procedures overshadowed advantages of the core model system and required significant efforts from the modeling team to fix them at the late stages of the project. In the current project framework we assume that most of the network coding and preparation work will be done by the DRCOG staff. The consultant role in this task would then be limited to the development of special network processing procedures for skimming the necessary level-of-service variables and (possibly) improvement of the existing assignment procedures. The assignment and skimming procedures need to be able to support multi-class highway assignment and various mode and mode combination transit-specific assignments. This task also includes validation of the highway and transit assignments, to be conducted when the entire model chain is calibrated and applied with feedback.

the p

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Table 7.2.5. Network Processing Procedures Level of Effort Hours by Staff Position

Main Sub-Tasks Senior PM/QC

Senior Modeler

Senior Programmer Modeler Programmer

Technical Support Total

Highway network capacity calculation 5 10 100 100 215 Highway assignment and skimming procedures 5 30 50 20 105 Transit assignment and skimming procedures 5 50 50 20 125 Highway network validation / auxiliary procedures 5 10 100 100 215 Transit network validation / auxiliary procedures 5 10 100 100 215 Total Hours by Staff Position 25 110 400 340 875

Task 6 - Population and Land-Use Data Preparation

In the overall model vision phase of the DRCOG model improvement initiative it is assumed that DRCOG will develop and apply an advanced land-use model that will be integrated with the new travel demand model. The scope of this task within the travel demand model improvement project relates to several specific technical components pertinent to the micro-simulation framework – preparation of the population Census and PUMS data for the population synthesizer as well as development of the household distribution models (curves). Table 7.2.6. Population and Land Use Data Preparation Level of Effort


PM/QC Senior

Modeler Senior


Target zonal values for population and land use for all years 10 10 50 100 170 Census data preparation for population synthesizer 10 20 50 80 PUMS data preparation for population synthesizer 10 20 40 70 Development of household distribution curves 10 50 20 80 Total Hours by Staff Position 40 140 210 400

Task 7 - Application Programming

This is the second major task (in addition to the core model development and estimation task) that requires a high degree of technical qualification as well as has a crucial impact on the whole project. The level of effort estimation for this task is heavily dependent on the past experience of the consultant team and the possibility of using generic pieces of software and subroutines for most of the model components. The PB Consult modeling team has developed a package of useful subroutines (Common Modeling Framework – CMF) based on the series of similar projects implemented in the other metropolitan areas that may be relevant in this context. This allows for

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a significant “economy of scale” and results in a comparatively limited budget relative to the complexity of the computerized model application. The most important sub-tasks within this task relate to the coding of choice model utilities, ancillary models, overall model system shell, development of the database corresponding to the model system, and development of the user interfaces for the model system management and reporting. These components are normally specific to the region and project and should be tailored specifically for DRCOG. This estimate assumes that approximately 50% of the total programming effort can be directly imported from the PB Consult CMF, thus the level of effort below corresponds to the 50% required to develop DRCOG-specific code. Table 7.2.7. Application Programming Level of Effort


PM/QC Senior

Modeler Senior


UEC coding and linking for the core set of models 100 300 200 300 900

Ancillary models 20 100 50 200 50 420 Overall shell for the model system 20 50 20 100 190 Data base development / management programs 120 200 400 720

GUI and reporting 30 50 100 50 200 450 Total Hours by Staff Position 310 750 320 1,200 20 2,680

Task 8 - Calibration and Sensitivity Analysis

This task corresponds to the final technical stage before the model system can be completed. Experience has shown that disaggregate estimation of the core choice models does not necessarily guarantee replication of the aggregate statistics of interests as well as traffic / transit counts and other independent sources of information used for the model validation. For this reason, it is necessary to carefully validate and adjust model parameters for the base year in order to replicate traffic counts and other aggregate targets with the necessary degree of accuracy. In addition to the static validation for the base year, it is useful to implement and analyze in detail several sensitivity tests for the base and future years with changing networks (new transportation facilities) or policy measures (for example, toll values). Table 7.2.8. Calibration and Sensitivity Analysis Level of Effort


PM/QC Senior

Modeler Senior


Base year calibration targets from all sources 10 100 100 100 310 Preparation of traffic and transit counts 20 50 400 470 Base year calibration runs, analysis and adjustments 100 100 50 50 100 400 Base year sensitivity analysis 10 10 10 10 50 90 Test runs for future year scenarios 10 20 20 50 50 50 200 Total Hours by Staff Position 20 250 130 260 110 700 1,470



Task 9 - Final Technical Report and User Manual

The model system development project requires a final technical report that documents the model structure, estimation, validation, and calibration. In addition to that, a comprehensive user manual for the developed pieces of software is needed for the DRCOG and other potential users and agencies. This task is allotted a comparatively small budget since a series of intermediate technical memos for all aspects of the model development is planned as a part of task 2. Thus, the final technical report and user manual would be a compendium of previously scripted documents. Table 7.2.9. Final Technical Report and User Manual Level of Effort


PM/QC Senior

Modeler Senior


Final technical report 40 80 16 140 40 316 Comprehensive user manual 8 40 40 180 120 40 428 Total Hours by Staff Position 48 120 56 320 120 80 744

Task 10 - Technical Support and Client Training

Experience has shown that the client’s technical staff will benefit from comprehensive training and technical support in order to take over the model system and be able to use it for practical planning on a daily basis. The model development effort includes a necessary 3-day training workshop as well as full technical support for the first 3 months following the model development and delivery to DRCOG. Table 7.2.10. Technical Support and Model Users Training


PM/QC Senior

Modeler Senior


Training workshop (3 days) 8 24 24 24 24 24 128 Technical support for the first 3 months / test runs 8 20 80 120 80 20 328 Total Hours by Staff Position 16 44 104 144 104 44 456

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