HAMPTON ROADS MODEL METHODOLOGY REPORT (Ver. 1.0) · VDOT Project ID:30681-01-02 Hampton Roads...

VDOT Project ID:30681-01-02 Hampton Roads Model Methodology Report Outline

Version 1.0 1

HAMPTON ROADS MODEL METHODOLOGY REPORT (Ver. 1.0) VDOT Project Number: 30681-01-02

Prepared for

Transportation & Mobility Planning Division Virginia Department of Transportation

By

December 2013

VDOT Project ID:30681-01-02 Hampton Roads Model Methodology Report Ver. 1.0

Version 1.0 2

TABLE OF CONTENTS

1 Introduction ........................................................................................................................................ 14

1.1 Model Enhancement Summary .......................................................................................... 14 1.2 Report Organization............................................................................................................. 15

2 Data Inputs ......................................................................................................................................... 16

2.1 TAZ Structure ....................................................................................................................... 16 2.2 Land Use Data ...................................................................................................................... 18 2.3 Area Type Procedure ........................................................................................................... 18 2.4 Highway Network ................................................................................................................ 20 2.5 Transit Networks .................................................................................................................. 23 2.6 Travel Surveys and Other Observed Data ........................................................................ 25 2.7 Traffic Counts ....................................................................................................................... 30 2.8 Speeds and Capacities .......................................................................................................... 31

3 Trip Generation .................................................................................................................................. 33

3.1 Internal-Internal Trips ......................................................................................................... 43 3.2 Internal-External Trips ........................................................................................................ 43 3.3 External-Internal Trips ........................................................................................................ 43 3.4 External-External Trips ....................................................................................................... 44 3.5 Initial Time of Day Procedures .......................................................................................... 46

4 Trip Distribution ................................................................................................................................ 48

4.1 Level of Service Inputs ........................................................................................................ 48 4.2 Gravity Model and Friction Factor Calibration ............................................................... 49 4.3 Internal Trips with 0 car/HH ............................................................................................. 51 4.4 Internal-External Trips ........................................................................................................ 59

5 Mode Choice ....................................................................................................................................... 60

5.1 Transit Paths and Skims ...................................................................................................... 60 5.2 Mode Choice Model Structure............................................................................................ 63 5.3 Mode Choice Model Calibration ........................................................................................ 65

6 Trip Assignment ................................................................................................................................. 67

6.1 Highway Assignment ........................................................................................................... 67 6.2 Volume Delay Functions ..................................................................................................... 69 6.3 Toll procedures ..................................................................................................................... 71 6.4 Transit Assignment............................................................................................................... 71

7 Heavy Truck Model ........................................................................................................................... 72

7.1 Trip Generation .................................................................................................................... 73 7.2 Trip Distribution................................................................................................................... 78 7.3 Time of Day .......................................................................................................................... 82 7.4 Truck Assignment ................................................................................................................ 82


Version 1.0 3

8 Feedback .............................................................................................................................................. 86

9 Static Validation .................................................................................................................................. 88

9.1 Trip Generation .................................................................................................................... 88 9.2 Trip Distribution................................................................................................................... 89 9.3 Mode Choice ......................................................................................................................... 99 9.4 Highway Assignment ........................................................................................................ 104 9.5 Speed Feedback and Volume Convergence .................................................................. 110 9.6 Comparison of Model and INRIX Speeds .................................................................... 110 9.7 Transit Assignment............................................................................................................ 111

10 Dynamic Validation ........................................................................................................................ 114

10.1 Sensitivity Tests with Changes in Land Use .................................................................. 114 10.2 Sensitivity Tests with Changes in Highway Network ................................................... 115 10.3 Sensitivity Tests with Changes in Travel Cost............................................................... 118 10.4 Sensitivity Tests with Changes in Transit Service ......................................................... 120 10.5 Future Year Test Forecast ................................................................................................ 122


Version 1.0 4

LIST OF TABLES

Table 2.1: Internal Zones ................................................................................................................................ 16

Table 2.2: External Zones............................................................................................................................... 17

Table 2.3: Year 2009 Aggregate Land Use Data ......................................................................................... 18

Table 2.4: Area Type Codes ........................................................................................................................... 18

Table 2.5: Area Type by Population and Employment Density ............................................................... 19

Table 2.6: Network Attributes ....................................................................................................................... 21

Table 2.7: Facility Type Codes ....................................................................................................................... 22

Table 2.8: Jurisdiction Codes ......................................................................................................................... 23

Table 2.9: HOV Codes .................................................................................................................................... 23

Table 2.10: NHTS Weighted Trips ............................................................................................................... 26

Table 2.11: Hampton Roads Linked Transit Trips by Trip Purpose for Surveyed Routes .................. 26

Table 2.12: Hampton Roads Linked Transit Trips by Trip Purpose for Un-Surveyed Routes ........... 27

Table 2.13: CTPP 2000 Journey-To-Work Flows ....................................................................................... 29

Table 2.14: Speed-Capacity Table Using Link Class ................................................................................... 32

Table 3.1: Seed Table for Household Stratification .................................................................................... 33

Table 3.2: Percent Household Distribution by Household Size ............................................................... 34

Table 3.3: Percent Household Distribution by Auto-Ownership Level .................................................. 35

Table 3.4: Household Stratifications by Household Size and Auto Ownership .................................... 36

Table 3.5: Trip Production Rates from NHTS for I-I and I-E Trips ...................................................... 38

Table 3.6: Trip Attraction Rates from NHTS for I-I Trips ....................................................................... 39

Table 3.7: Area Type Adjustments to Trip Productions and Attractions................................................ 42

Table 3.8: Passenger Car E-E Shares ............................................................................................................ 45


Version 1.0 5

Table 3.9: Cordon Travel ................................................................................................................................ 45

Table 3.10: Time Periods ................................................................................................................................ 46

Table 3.11: Peak and Off-Peak Factors ........................................................................................................ 47

Table 4.1: Terminal Times .............................................................................................................................. 48

Table 4.2: Comparison of Travel Times in Minutes ................................................................................... 49

Table 4.3: Comparison of NHTS vs. Model Trips for 0 Car/HH – Peak HBW ................................... 53

Table 4.4: Comparison of NHTS vs. Model Trips for 0 Car/HH – Offpeak HBW ............................ 54

Table 4.5: Comparison of NHTS vs. Model Trips for 0 Car/HH – Peak HBS .................................... 55

Table 4.6: Comparison of NHTS vs. Model Trips for 0 Car/HH – Offpeak HBS .............................. 56

Table 4.7: Comparison of NHTS vs. Model Trips for 0 Car/HH – Peak HBO ................................... 57

Table 4.8: Comparison of NHTS vs. Model Trips for 0 Car/HH – Offpeak HBO ............................. 58

Table 5.1: Model Weights for In-Vehicle and Out-of-Vehicle Times ..................................................... 60

Table 5.2: Delay by Facility and Area Type in Minutes/Mile in Peak ..................................................... 62

Table 5.3: Delay by Facility and Area Type in Minutes/Mile in Off-Peak .............................................. 62

Table 5.4: Mode Choice Model Coefficients ............................................................................................... 65

Table 6.1: Time of Day Factors ..................................................................................................................... 67

Table 6.2: Factors for P-A to O-D Conversion .......................................................................................... 68

Table 6.3: Capacity Factors by Time of Day................................................................................................ 69

Table 6.4: VDF Values .................................................................................................................................... 70

Table 6.5: Auto and Truck Tolls .................................................................................................................... 71

Table 7.1: Employment Allocation by NAICS ............................................................................................ 74

Table 7.2: Truck Area Type Factors.............................................................................................................. 75

Table 7.3: Truck and Port Zones................................................................................................................... 77

Table 7.4: Average Trip Length Ratios per Trip Type ............................................................................... 79


Version 1.0 6

Table 7.5: Average Trip Length Ratios for Trucks from Other Models ................................................. 79

Table 7.6: Synthesized Truck Average Trip Length ................................................................................... 80

Table 7.7: Truck F Factor Gamma Coefficients ......................................................................................... 81

Table 7.8: Truck ATL Comparisons ............................................................................................................. 82

Table 7.9: Truck Detailed Time of Day Splits ............................................................................................. 82

Table 7.10: Truck-Restricted Roadways ....................................................................................................... 84

Table 8.1: Feedback Convergence Criterion ................................................................................................ 87

Table 9.1: Trip Generation Results ............................................................................................................... 88

Table 9.2: Average Trip Length by Time and Distance ............................................................................. 89

Table 9.3: Comparison of 2009 Average Weekday Model vs. NHTS Peak HBW Trips by Jurisdictions ............................................................................................................................................. 91

Table 9.4: Comparison of 2009 Average Weekday Model vs. NHTS Peak HBS Trips by Jurisdictions ................................................................................................................................................................... 92

Table 9.5: Comparison of 2009 Average Weekday Model vs. NHTS Peak HBO Trips by Jurisdictions ............................................................................................................................................. 93

Table 9.6: Comparison of 2009 Average Weekday Model vs. NHTS Peak NHB Trips by Jurisdictions ............................................................................................................................................. 94

Table 9.7: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak HBW Trips by Jurisdictions ............................................................................................................................................. 95

Table 9.8: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak HBS Trips by Jurisdictions ............................................................................................................................................. 96

Table 9.9: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak HBO Trips by Jurisdictions ............................................................................................................................................. 97

Table 9.10: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak NHB Trips by Jurisdictions ............................................................................................................................................. 98

Table 9.11: Mode Choice Model Constants (Initial Calibration) .............................................................. 99

Table 9.12: Mode Choice Model Constants (Final implementation of HR Model) ........................... 100

Table 9.13: Mode Choice Model Constants in equivalent IVTT minutes (Initial Calibration) ......... 101


Version 1.0 7

Table 9.14: Target Values vs. Mode Choice Model Results (Initial calibration) .................................. 102

Table 9.15: Target Values vs. Mode Choice Model Results (Final Implementation) ......................... 103

Table 9.16: Validation Criteria and Targets ............................................................................................... 104

Table 9.17: Highway Validation at Screen Lines ...................................................................................... 107

Table 9.18: Highway Validation on Screen Lines using Facility Type .................................................. 108

Table 9.19: % RMSE in Highway Validation by Facility Type .............................................................. 108

Table 9.20: % RMSE by Functional Class for Screen Lines................................................................... 109

Table 9.21: % RMSE and R-Square for Entire Model Region .............................................................. 109

Table 9.22: % RMSE and Volume/Count Ratio for Trucks .................................................................. 109

Table 9.23: Volume Convergence for each model Feedback Iteration ................................................ 110

Table 9.24: Comparison of Model versus INRIX Congested Speed by Facility Type ....................... 111

Table 9.25: Average Weekday Boardings on HRT and WATA Buses ................................................. 111

Table 9.26: Comparison of Survey versus Modeled Average Weekday Boardings on HRT Buses by Peak and Off-Peak Periods – For HRT Bus Routes Surveyed (Initial validation) .................... 112

Table 9.27: Comparison of Survey versus Modeled Average Weekday Boardings for HR model with HRT changes ........................................................................................................................................ 113

Table 10.1: Changes in Land Use ............................................................................................................... 115

Table 10.2: Removal of Major Highway Link ........................................................................................... 115

Table 10.3: Addition of Major Highway Link .......................................................................................... 116

Table 10.4: Addition of Lane of Capacity to Major Highway Link ....................................................... 117

Table 10.5: Change in Free Flow Speed .................................................................................................... 117

Table 10.6: Addition of $5 Toll on Midtown Tunnel, Downtown Tunnel and High Rise Bridge ... 118

Table 10.7: Increase in VOT by 50%......................................................................................................... 119

Table 10.8: Increase in Gas Cost to $5/Gallon ........................................................................................ 119

Table 10.9: Free Transit Fares ..................................................................................................................... 120


Version 1.0 8

Table 10.10: Doubled Transit Fares ........................................................................................................... 121

Table 10.11: Increase in Transit Headways by 50% ................................................................................ 121

Table 10.12: Decrease in Transit Headways by 50% ............................................................................... 121

Table 10.13: Addition of New Transit Route ........................................................................................... 122


Version 1.0 9

LIST OF FIGURES

Figure 2.1: Area Type on Hampton Roads Network ................................................................................. 20

Figure 2.2: Transit Lines in Hampton Roads Model .................................................................................. 25

Figure 2.3: Links with AWDT Counts ......................................................................................................... 31

Figure 3.1: Stratification of Households by Household Size by Auto Ownership ................................ 37

Figure 3.2: Stratification of Households by Auto Ownership by Household Size ................................ 37

Figure 3.3: Districts for Evaluating Attraction Rates ................................................................................. 40

Figure 3.4: Accessibility Values ...................................................................................................................... 42

Figure 4.1: Friction Factor Calibrated Curves for Peak Period................................................................. 50

Figure 4.2: Friction Factor Calibrated Curves for Off-Peak Period ......................................................... 51

Figure 4.3: Districts for 0 car/HH analysis .................................................................................................. 52

Figure 4.4: Friction Factor Calibrated Curves for I-E Trips ..................................................................... 59

Figure 5.1: Observed vs. Modeled End-to-End Bus Run Times.............................................................. 63

Figure 5.2: Mode Choice Nesting Structure ................................................................................................. 64

Figure 6.1: Volume Delay Function Curve .................................................................................................. 70

Figure 7.1: FHWA Vehicle Classification System ....................................................................................... 72

Figure 7.2 Truck External Share Model ....................................................................................................... 76

Figure 7.3: Truck Zone Map .......................................................................................................................... 78

Figure 7.4: Truck F Factors ............................................................................................................................ 81

Figure 7.5: Truck-Restricted Roadways ........................................................................................................ 85

Figure 9.1: Trip Length Frequency Distribution by Time – Peak Purposes ........................................... 90

Figure 9.2: Trip Length Frequency Distribution by Time – Off-peak Purposes ................................... 90

Figure 9.3: Locations of Screen Lines ........................................................................................................ 106


Version 1.0 10

Figure 9.4: Links for Comparison of Modeled versus INRIX Congested Speed ............................... 110


Version 1.0 11

GLOSSARY OF ACRONYMS

ADT = Average Daily Traffic

AAWDT = Annual Average Weekday Daily Traffic

AQ = Air Quality Conformity

AWDT = Average Weekday Daily Traffic

ATL = Average Trip Length

CBD = Central Business District

COA = Comprehensive Operations Analysis

CTPP = Census Transportation Planning Package

E-E = External-External Trips

E-I = External-Internal Trips

FF = Friction Factor

FHWA = Federal Highway Administration

FTA = Federal Transit Administration

GIS = Geographic Information System

HBW = Home Based Work

HBO = Home Based Other

HBS = Home Based Shopping

HBSR = Home Based Social/Recreation

HCM = Highway Capacity Manual

HOV = High Occupancy Vehicle

HRT = Hampton Roads Transit

HRTPO = Hampton Roads Transportation Planning Organization

HTS = Household Travel Survey

I-E = Internal-External Trips

I-I = Internal-Internal Trips

ITS = Intelligent Transportation System

IVTT = In Vehicle Travel Time

LOS = Level of Service

LUD = Land Use Density

MPO = Metropolitan Planning Organization

MSA = Method of Successive Averages

NAAQS = National Ambient Air Quality Standards


Version 1.0 12

NAICS = North American Industry Classification System

NCHRP = National Cooperative Highway Research Program

NCRTPB = National Capital Region Transportation Planning Board

NHB = Non-Home Based

NHBO = Non Home Based Other

NHBW = Non Home Based Work

NHI = National Highway Institute

NHTS = National Household Transportation Survey

NTD = National Transit Database

O-D = Origin-Destination

OVTT = Out of Vehicle Travel Time

P-A = Production-Attraction

PDC = Planning District Commission

PNR = Park N Ride

PT = Public Transportation

RMSE = Root Mean Square Error

SOV = Single Occupant Vehicle

TAZ = Transportation Analysis Zone

TIP = Transportation Improvement Program

TLFD = Trip Length Frequency Distribution

TMIP = Travel Model Improvement Program

TMS = Traffic Monitoring System. Serves as the Virginia Department of Transportation’s official traffic database.

ToD = Time of Day

TRB = Transportation Research Board

TDFM = Travel Demand Forecasting Model

TWLTL = Two Way Left Turn Lane

V/C = Volume/Capacity

VDF = Volume Delay Function

VDOT = Virginia Department of Transportation

VEC = Virginia Employment Commission

V/C = Volume to Capacity Ratio

VHT = Vehicle Hours of Travel


Version 1.0 13

VMASC = Virginia Modeling, Analysis and Simulation Center

VMT = Vehicle Miles of Travel

VTM = Virginia Transportation Modeling

VSM = Virginia Statewide Travel Demand Model

WATA = Williamsburg Area Transit Authority


Version 1.0 14

1 Introduction

The Hampton Roads regional travel demand model represents an advanced practice four-step forecasting model to support air quality analysis and project planning in the Hampton Roads region. This report will document the methodology employed to develop the Hampton Roads Model and will document the model validation against observed data sources.

1.1 Model Enhancement Summary

The Hampton Roads regional travel demand model has been enhanced to better reflect the transportation supply (networks) with a variety of improvements to enhance its ability to forecast regional travel demand. These improvements generally follow the guidelines established in the Virginia Transportation Modeling (VTM) Policies and Procedures Manual. The following is a list of the major changes from the previous version of the Hampton Roads model.

• The highway network has been enhanced and provides significantly more detail in terms of streets and their alignments. The freeway interchanges and major arterials are micro-coded in the network (i.e., coded more closely to the way they actually exist on the ground).

• The transit networks and their processes were converted into CUBE Public Transport (PT) module. The networks were updated to accurately reflect 2009 Hampton Roads Transit (HRT) and Williamsburg Area Transit Authority (WATA) transit services.

• The model has been refined to conduct full time-of-day modeling. The first three steps in the model (trip generation, trip distribution and mode choice) are stratified for the peak period and the off-peak period separately. The highway assignments are further stratified into four time periods – AM peak, Midday, PM peak and Night.

• The refined trip generation and distribution models make extensive use of the 2009 National Household Travel Survey (NHTS). Key relationships such as trip rates by purpose, average trip lengths, and trip frequency distributions are based on that survey.

• The mode choice model was developed using a variety of data sources, including the version of the Hampton Roads model used for the Norfolk Tide forecasts (Source: Norfolk LRT Project Final Design Patronage Forecasting Report, 2007). These include the Comprehensive Operations Analysis (COA) survey (Source: Comprehensive Operations Analysis for Hampton Roads Transit, 2009), NHTS data (automobile occupancy) and model parameters based on experience of FTA reviews. The mode choice model is executed using the CUBE XCHOICE module.

• The highway assignment procedures include a variety of enhancements. These include the use of Conical Volume-Delay functions built up on the VDF optimization research done by Virginia Modeling, Analysis and Simulation Center (VMASC) at Old Dominion University (Source: Evaluation of Volume-Delay Functions and Their Implementation in VDOT Travel Demand Models, May 2011), refinements to the speed-capacity tables and the use of enhanced toll procedures.

• A new heavy truck model was developed and validated. • The model has been updated to include a feedback loop, which ensures that speeds from the

resulting highway assignments go through the feedback during forecasting process.


Version 1.0 15

• The model has been generally calibrated and validated to the standards defined in the VTM Policies and Procedures Manual.

• The final implementation of the HR model includes the changes made by Hampton Roads Transit (HRT) and its consultant independent to this project for the TIDE rail corridor validation, as part of the ongoing Virginia Beach Transit Extension Study (VBTES). (Source: VBTES Calibration and Validation of the Ridership Model Report, December 2012) .The key changes were in the transit path-building and mode choice procedures which had minimal impact on overall highway validation.

1.2 Report Organization

This model development and validation report is divided into 10 chapters.

• Chapter 1, Introduction, describes the model enhancements and report organization. • Chapter 2, Data Inputs, describes the input data for the Hampton Roads model. • Chapter 3, Trip Generation Model, explains the methodology for trip generation along with

trip production and attraction rates, internal trips, and external trips. • Chapter 4, Trip Distribution Model, provides a description of the trip distribution model and

calibration of the friction factors. • Chapter 5, Mode Choice, provides a description of the transit skimming procedures and the

methodology for the mode choice calibration using XCHOICE. • Chapter 6, Trip Assignment, explains the time of day procedures, volume delay functions,

toll procedures, and transit assignment. • Chapter 7, Truck Model, explains the truck model and its methodology. • Chapter 8, Feedback, describes the speed feedback procedure and volume convergence

criteria. • Chapter 9, Static Validation, shows the calibration and validation results for trip generation,

trip distribution, mode choice, highway assignments and transit assignments. • Chapter 10, Dynamic Validation, shows the sensitivity of the model to various parameters

like changes in land use, highway network, travel costs and transit service. • List of References for HR model development, calibration and validation.


Version 1.0 16

2 Data Inputs

This chapter describes the methodology for the data inputs to the Hampton Roads travel demand model. The following is a list of data inputs explained in this chapter:

• TAZ Structure • Land Use Data • Area Type Procedure • Highway Network • Transit Networks • Travel Surveys and Other Observed Data • Traffic Counts • Speeds and Capacities

2.1 TAZ Structure

The Traffic Analysis Zones (TAZ) in the Hampton Roads modeling area are defined from 1-1503, which includes a few spare zone numbers for future use. TAZs 1-1460 and 1500-1503 are internal zones while TAZs 1461-1490 are external stations. The modeling area comprises 13 jurisdictions – Gloucester County, Isle of Wight County, James City County, York County, City of Chesapeake, City of Hampton, City of Newport News, City of Norfolk, City of Poquoson, City of Portsmouth, City of Suffolk, City of Williamsburg and City of Virginia Beach. The external zones are defined as streets exiting the modeled area. The list of internal zones and their jurisdictions are shown in Table 2.1. The list of external zones and their description is shown in Table 2.2.

Table 2.1: Internal Zones

Jurisdiction Name TAZ Chesapeake 352-448, 582-687, 860-881 Norfolk 1-208, 902, 907 Portsmouth 449-508, 910-917 Suffolk 510-581, 692-724, 831-859 Virginia Beach 209-351, 585-672, 885-895 Isle of Wight 726-746 Newport News 1101-1205 Hampton 1001-1097 Poquoson 1224-1234 Williamsburg 1259-1280 James City County 1306-1351 York 1363-1406 Gloucester 1427-1447, 1500-1503


Version 1.0 17

Table 2.2: External Zones

External Zones Streets

1461 Route 13 1462 Princess Anne Road 1463 Blackwater Road 1464 Chesapeake Expressway 1465 Highway 17 1466 Desert Road 1467 Carolina Road 1468 Adams Swamp Road 1469 Whaleyville Boulevard 1470 Pittmantown Road 1471 Franklin Bypass 1472 Carrsville Highway 1473 Blackwater Road 1474 Windsor Boulevard 1475 W Broadwater Road 1476 State Route 626 1477 Old Stage Highway 1478 New Kent Highway 1479 I-64 1480 Richmond Highway 1481 Route 5 1482 Jamestown Road 1483 John Clayton Memorial Highway 1484 Buckley Hall Road 1485 Quay Road 1486 George Washington Memorial Highway 1487 Lewis B Puller Memorial Highway 1488 Adner Road 1489 Mill Swamp Road 1490 Great Fork Road 1491 New 460 (Future Zone)


Version 1.0 18

2.2 Land Use Data

The Hampton Roads modeling area uses year 2009 household and employment data compiled at the TAZ level, as provided by the Hampton Roads Transportation Planning Organization (HRTPO). The data includes households, population, autos, retail employment and non-retail employment. The land use data for population and households are used in estimating trip productions and the retail and non-retail employment data is used in estimating the trip attractions in the modeling area. Table 2.3 shows the aggregate land use data for the Hampton Roads modeling region.

Table 2.3: Year 2009 Aggregate Land Use Data

Total Households 606,902 Total Population 1,627,273 Total Autos 1,263,199 Total Retail Employment 187,111 Total Non-Retail Employment 853,826

2.3 Area Type Procedure

The Hampton Roads network uses a set of five standard area type definitions. Area types represent the level of development in a TAZ, which is coded to represent the level of development adjacent to each roadway link in that TAZ. The area type of a zone is a function of the population and employment densities within the zone, including all other zones whose centroid lies within 1 mile straight-line distance of the subject zone’s centroid. Based on the densities in the entire modeling region, area types are categorized as Central Business District (CBD), Urban, Dense Suburban, Suburban and Rural. Table 2.4 shows the area type codes and their description.

Table 2.4: Area Type Codes

Area Type Code

Area Type Description

1 CBD 2 Urban 3 Dense Suburban 4 Suburban 5 Rural

Table 2.5 shows the population and employment densities and the corresponding area types. The CBD area type was defined manually for TAZes 1, 3 and 4, as selected at the discretion of HRTPO and VDOT. A separate attribute called R_AREATYPE in the master network lets the user define the area type codes on the network links. In addition to the area type codes for the CBD region, freeways are coded through this attribute which overrides the area type computed through


Version 1.0 19

automated procedure in the model. If the attribute is left blank, the area type procedure calculates the area type for the links based on the land use data.

Table 2.5: Area Type by Population and Employment Density

Pop Density Employment Density

< 3.423 3.423 to 3.769 3.769 to 4.462 4.462 to 5.155 5.155 to 5.849 5.849 to 6.542 > 6.542

< 3.849 Rural Rural Suburban Dense Suburban Dense Suburban Dense Suburban Urban

3.849 to 4.552 Rural Rural Suburban Dense Suburban Dense Suburban Dense Suburban Urban

4.552 to 5.957 Suburban Suburban Suburban Dense Suburban Dense Suburban Dense Suburban Urban

5.957 to 7.362 Suburban Suburban Suburban Dense Suburban Dense Suburban Dense Suburban Urban

7.362 to 8.767 Dense Suburban Dense Suburban Dense Suburban Dense Suburban Dense Suburban Dense Suburban Urban

8.767 to 10.17 Dense Suburban Dense Suburban Dense Suburban Dense Suburban Dense Suburban Dense Suburban Urban

> 10.17 Urban Urban Urban Urban Urban Urban Urban

Figure 2.1 shows the area type by link for the modeling area.


Version 1.0 20

Figure 2.1: Area Type on Hampton Roads Network

2.4 Highway Network

The Hampton Roads highway network was developed in CUBE. The enhanced base network is quite detailed and was created using aerial photographs. The refined network includes approximately 39,480 links and covers freeways, major arterials, minor arterials and major collectors in the modeling area. The network also includes minor collectors and local streets to provide appropriate connectivity in the network. The highway network contains link attributes defined by VDOT in the Policy and Procedures Manual. They include Distance, Route Name, Facility Type, Area Type, Speed Class, Capacity Class, and Link Capacity that are used in the development of highway level of


Version 1.0 21

service estimates (time and costs) and assignment procedures. A complete list of the standard link attributes is shown in Table 2.6. The link attributes were developed using Road Network System (RNS) database (snapshot from RNS in late 2010), NAVTEQ data and the old Hampton Roads model network.

Table 2.6: Network Attributes

No. Link Variable Description Data Type

Need

1 ANODE Beginning node of model network link Numeric Model uses 2 BNODE Ending node of model network link Numeric Model uses 3 DISTANCE Highway Link distance in miles Numeric Model uses 4 LANES Number of DIRECTIONAL through lanes Numeric Model uses 5 FACTYPE Facility Type used for Modeling Only Character Model uses 6 TWLTL Two Way Left Turn Lane Character Model uses 7 ONEWAY Directionality Indicator Numeric Model uses 8 TRK_PHB Truck Prohibition Identifier Character Model uses 9 POST_SPD Posted Speed Limit in miles per hour (mph) Numeric Model uses 10 SPDCLASS Speed class code from speed lookup table for the region Numeric Model uses 11 LINK_CAP Link Capacity in vehicles/lane/hour if known Numeric Model uses 12 CAPCLASS Capacity class code from capacity lookup table for the region Numeric Model uses 13 AWDT Observed 24 hour average weekday count for Base Year Numeric Model uses 14 RTE_NAME Local street name (911) Character Network Coding 15 RTE_NO Official State highway route # (Federal Aid Number) Character Network Coding 16 PROJ_ID Project ID used by VDOT and/or MPO Character Network Coding 17 YR_OPEN Estimated year highway project open for traffic Character Network Coding 18 YR_CLOSE Estimated year highway project closed to traffic Character Network Coding 19 JURIS_NO VDOT's city/county jurisdiction code Character Reporting 20 FEDFUNC Federal functional class Character Reporting 21 AREATYPE Land use ID: Five types Character Reporting 22 FEDAT Federal Area Type: Urban or Rural Numeric Reporting 23 MPO_ID Identifier for which MPO region link belongs to. Character Reporting 24 SCRLN_ID Screenline Identifier Character Reporting 25 CORD_ID Cordon Line Identifier Character Reporting 26 CUTLN_ID Cutline Identifier Character Reporting 27 TMS_ID TMS Count Station ID Character State Database Connection 28 RTE_ID HTRIS Route ID Character State Database Connection 29 BEGIN_MP Beginning Milepoint of a link Numeric State Database Connection 30 END_MP Ending Milepoint of a link Numeric State Database Connection 31 HOVTYPE HOV Type Identifier Character Model uses 32 TOLL_GRP Toll Group Numeric Model uses 33 TOLLGATE Toll Gate Group representing delay at toll barrier Numeric Model uses 34 R_AREATYPE Area Type defined by User Character Network Coding 35 R_FFLOWSPEED Free Flow Speed defined by User Numeric Network Coding 36 R_LINK_CAP Link Capacity defined by User Numeric Network Coding

Facility types classify roadway links according to their function and/or design characteristics whereas the area types represent the development density near each link. A combination of the area type and


Version 1.0 22

facility type is used in representing the speeds and capacities of the roadway facilities. The definitions of the 12 facility types are shown in Table 2.7.

Table 2.7: Facility Type Codes

Facility Type Code

Facility Type Name

1 Interstate/Principal Freeway 2 Minor Freeway 3 Principal Arterial/Highway 4 Major Arterial/Highway 5 Minor Arterial/Highway 6 Major Collector 7 Minor Collector 8 Local 9 High Speed Ramp 10 Low Speed Ramp 11 Centroid Connector 12 External Station Connector

The distances on the links represent the real distances in miles. Links are coded as either one-way or two-way links. Each link is coded with number of lanes by direction. A 2009 average weekday daily traffic count (AWDT) is coded on links wherever available. The links to be used in highway validation process are coded with screenline numbers. Each link is coded with a route name and the VDOT route number. Links are also defined by jurisdiction codes as shown in Table 2.8. Two-Way-Left-Turn-Lanes (TWLTL) as well as truck prohibitions are also indicated in the network.


Version 1.0 23

Table 2.8: Jurisdiction Codes

Code Jurisdiction 073 Gloucester Co 093 Isle of Wight Co 095 James City Co 199 York Co 550 Chesapeake City 650 Hampton City 700 Newport News City 710 Norfolk City 735 Poquoson City 740 Portsmouth City 800 Suffolk City 810 Virginia Beach City 830 Williamsburg City

The definition of the High Occupancy Vehicle (HOV) lanes is done through a link attribute called HOVTYPE. The attribute is defined by a 4-character code (see Table 2.9). The first character shows the vehicle occupancy in the AM peak period, the second character shows the vehicle occupancy in midday, the third character shows the vehicle occupancy in the PM peak period and the last character shows the vehicle occupancy at night. For example: code “2111” indicates that the lane is HOV-2 in the AM peak and is SOV during midday, PM peak and night. Similarly, code “9121” indicates that the lane is non-operational in the AM peak, SOV during midday, HOV-2 during PM peak and SOV at night. Code “9999” represents transit only links.

Table 2.9: HOV Codes

Code Meaning If Lane is not HOV then:

0 or ' ' All vehicles allowed If Lane is HOV then:

1 All vehicles allowed 2 HOV2+ only 3 HOV3+ only 9 Closed to all vehicles

2.5 Transit Networks

The transit routes operated by Hampton Roads Transit (HRT) and Williamsburg Area Transit Authority (WATA) in 2009 are coded in the CUBE network. A private bus service from Gloucester to Newport News operated by Newton Bus Company is also coded. HRT routes include the local


Version 1.0 24

buses and the MAX (express) buses in Hampton, Chesapeake, Newport News, Norfolk, Portsmouth, Suffolk, and Virginia Beach. The transit network also includes Paddlewheel Ferry, Virginia Beach Wave, and the NET. The transit networks were coded to replicate the scheduled service, routing patterns and stops for peak period and off-peak periods , the peak period being 6-9 am and 3-6 pm, and the off-peak period being 9am-3pm and 6pm-6am. The transit networks and the processes are stored and executed within the Public Transport (PT) module in CUBE Voyager. Transit access, egress and transfer links are built using PT procedures. Park-N-Ride (PNR) access to transit are defined using the highway network to simulate drive-to-transit opportunities. The base year networks consists of seven PNR lots: Silver Leaf, Greenbrier Mall, Indian River, Magnolia, Portsmouth Park and Sail Lot, Route 17/Hayes Plaza in Gloucester, and Courthouse in Gloucester.

The transit networks also include a fringe-park transit mode for the HBW trip purpose in the peak period. The fringe transit mode represents the opportunity of the daily commuters to park their cars in the outskirts of the CBD and then either walk to their workplace or ride employer shuttles or utilize transit. This differs from the traditional Park-N-Ride in that the majority of the trip is done by using an automobile. Harbor Park, Harrison Opera House and Lot 39 are the fringe parking locations in the base year network.

The following modes have been used in the Hampton Roads model:

• Modes 1-2 represent HRT local buses • Mode 6 represents WATA buses • Modes 3, 4, 5 and 9 represent WAVE, Ferry, NET and MAX respectively • Modes 7, 8 and 9 represent Fringe • Mode 11 represents LRT • Modes 16, 15 and 12 represent walk access, drive access and transfer respectively • Mode 17 represents walk access/egress for WATA buses

Section 5.1 gives more explanation of the transit path building parameters and skimming process.

Figure 2.2 shows the plot of transit routes in the Hampton Roads network.


Version 1.0 25

Figure 2.2: Transit Lines in Hampton Roads Model

2.6 Travel Surveys and Other Observed Data

The following section explains the application of 2009 National Household Travel Survey (NHTS) Virginia Add-On, the transit survey in the form of Comprehensive Operations Analysis (COA) surveys supplemented by additional surveys done for the Virginia Beach Fixed-Guideway project, the 2000 Census Transportation Planning Package (CTPP) Journey-To-Work survey, and the 2010 INRIX data (www.inrix.com) for the Hampton Roads model development.

2.6.1 Household Survey

The 2009 National Household Travel Survey (NHTS) Virginia Add-On was conducted for VDOT by the Federal Highway Administration (FHWA). The NHTS data is the primary source of data used in developing the Hampton Roads model. The NHTS contains information on both local and long-distance travel such as the mode of transportation, duration of the trip, purpose of the trip and the geographic location of the origin and destination. The NHTS was used to derive trip rates, trip distribution patterns by purpose, average trip lengths by purpose, trip length frequencies, time of day distributions and automobile occupancy. This information was used extensively in calibration and

http://www.inrix.com/


Version 1.0 26

validation in each of the steps of the updated travel demand model. Table 2.10 shows the NHTS weighted person trips for the Hampton Roads region by purpose.

Table 2.10: NHTS Weighted Trips

Purpose NHTS Weighted Trips

Internal-Internal HBW 894,152 Internal-Internal HBS 1,084,301 Internal-Internal HBO 1,976,488 Internal-Internal NHB 1,473,377 Internal-External Work 24,961 Internal-External Non Work 168,260 Total 5,621,539

2.6.2 Transit Survey

Comprehensive Operations Analysis (COA) surveys supplemented by additional surveys done for the Virginia Beach Fixed Guideway project are used as a source of ridership information in modeling transit. The observed walk to transit and drive to transit linked trips counted in those surveys serve as targets in the mode choice calibration procedure. Section 9.3, Page 99 shows the transit targets used in mode choice calibration. The boardings by route in the peak and off-peak period are used in transit assignment validation. Table 2.11 shows the linked weighted transit trips from the survey data. Since the surveys do not cover all HRT routes, the total ridership statistic from the National Transit Database (NTD) for 2009 (available at www.ntdprogram.gov) is used as an additional source of data for transit validation. Table 2.12 shows the linked trips for the un-surveyed HRT routes and WATA buses.

Table 2.11: Hampton Roads Linked Transit Trips by Trip Purpose for Surveyed Routes

Purpose Linked Transit Trips

HBW 17,694 HBO 10,711 NHB 3,737 Total 32,142

http://www.ntdprogram.gov/


Version 1.0 27

Table 2.12: Hampton Roads Linked Transit Trips by Trip Purpose for Un-Surveyed Routes

Un-Surveyed HRT and WATA Trips

2009 NTD HRT Avg. Weekday Unlinked Trips 53,465 Surveyed Avg. Weekday Unlinked Trips 45,500 Un-Surveyed Avg. Weekday Unlinked Trips 7,965 2009 WATA Avg. Weekday Unlinked Trips 3,240 Un-Surveyed HRT and WATA Unlinked Trips 11,205 Un-Surveyed HRT and WATA Linked Trips 7,891 HBW 4,344 HBO 2,630 NHB 917 Note: Overall survey transfer ratio assumed as 1.42, based on the transfer ratio of surveyed routes

2.6.3 Census Transportation Planning Package (CTPP) Survey

The 2000 Census Transportation Planning Package (CTPP) survey is also used as an additional source of information for validating the patterns of home-to-work trip flows as a part of the trip distribution validation.


Version 1.0 28

Table 2.13 shows 2000 CTPP journey-to-work flows by jurisdictions. The jurisdiction of the worker’s residence is shown on the left side and the jurisdiction of the worker’s workplace is shown across the top; the cell values represent the number of workers.


Version 1.0 29

Table 2.13: CTPP 2000 Journey-To-Work Flows

District Chesapeake Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News

Hampton City Poquoson W'burg

James City

York Cnty Gloucester Total

Chesapeake 36,378 24,904 9,977 1,850 15,394 294 1,737 1,095 18 87 72 211 17 92,032 Norfolk City 6,877 70,261 4,380 588 17,717 120 1,955 1,576 0 138 125 241 13 103,990 Portsmouth 7,618 8,430 18,672 1,644 2,942 462 1,228 623 0 15 52 125 7 41,817 Suffolk 3,190 3,528 3,440 10,826 1,644 1,615 1,397 915 22 29 40 48 0 26,695 VA Beach 18,542 55,961 7,317 1,223 121,786 205 2,319 2,020 51 100 136 499 26 210,186 Isle of Wight 526 674 787 1,284 281 4,690 2,544 1,160 32 88 131 131 0 12,327 Newport News 879 3,796 842 384 1,316 394 47,006 16,110 273 2,392 3,496 5,106 319 82,314 Hampton City 868 5,704 915 308 1,490 358 16,709 33,270 330 620 935 1,673 121 63,302 Poquoson 109 173 71 31 57 16 1,437 1,613 1,068 91 96 552 13 5,327 Williamsburg 14 46 10 7 53 22 314 164 0 2,041 822 324 38 3,856 James City 139 274 82 48 224 39 2,536 893 14 4,498 8,299 2,095 100 19,242 York County 284 1,033 281 156 496 45 7,640 6,883 270 1,672 1,953 5,974 241 26,929 Gloucester 149 319 100 41 128 6 3,311 1,033 6 554 923 1,505 6,305 14,379 Total 75,574 175,102 46,873 18,390 163,528 8,265 90,134 67,355 2,084 12,326 17,080 18,485 7,200 702,395


Version 1.0 30

2.6.4 INRIX

Speed data, collected by INRIX (www.inrix.com) in 2010 was also used as a source of information for the free-flow speeds, congested speeds and were used in the calibration of volume-delay functions (VDF) and the congested speeds. Section 2.8, Page 31 and Section 9.6, Page 110 provide more details about the application of the INRIX data.

2.7 Traffic Counts

The traffic count information was compiled using VDOT’s official Traffic Monitoring System (TMS). The TMS counts are used in the validation of the highway assignments. Year 2009 daily and hourly counts are available at various TMS locations. Some TMS locations have hourly count information for 2007, 2008 and 2010. For the purposes of validation of the Hampton Roads model, 2009 counts are given preference followed by 2010, 2008 and 2007. The hourly counts are combined into the four modeled time periods:

– AM Peak Period (6-9 AM) – Midday (9 AM to 3PM) – PM Peak Period (3 to 6 PM) – Night (6 PM to 6 AM)

The time-of-day procedures are described in greater detail in Section 3.5, Page 46 and Table 3.10, Page 46. These counts are used in validating the four time period highway assignments. Figure 2.3 shows the links (in red) where counts are available in the Hampton Roads model network. The counts were checked for consistency. Duplicated counts, illogical values, and counts that were inconsistent with the network topology were removed from the database. The total AWDT in the database for the freeways represents daily counts on both general purpose and HOV lanes together. These counts were split based on prior model runs to compute AWDT for general purpose lanes and HOV lanes separately.

Note: The HOV links where the AWDT was split and also few other links in the network with manual overrides for AWDT do not have TMSID coded on the link.



Version 1.0 31

Figure 2.3: Links with AWDT Counts

2.8 Speeds and Capacities

Speeds and capacities of the highway links are in the form of look-up tables based on the link class. The link class is a combination of the area type and facility type of the links. The last two digits indicate the area type and the first digit or two indicates the facility type. For example: Link class 105 indicates a freeway link in rural area that falls under facility type of 1 and area type of 5. Similarly, 1203 indicates an external connector in a dense suburban area. The speed-capacity information was initially taken from the old Hampton Roads model. The free flow speeds were then updated using the INRIX observed speed data. The free flow speeds were also adjusted to avoid big differences between the posted speed limits and the free flow speeds. Later, the speeds and capacities were updated as a part of the assignment validation effort. The free flow speeds are an initial feed to the Volume Delay functions. The final speed-capacity table used in Hampton Roads model is shown in Table 2.14. The link capacity defined as vehicle/per lane/per hour is used in calculating the overall capacity of the facilities during the highway assignments.

The free flow speeds and capacities on highway links can also be manually adjusted by the user. The link attributes R_LINK_CAP and R_FFLOWSPEED allow the user to code link capacity and free flow speed in the master network. If the values are not coded by the user and the attributes left blank, the speed-capacity lookup table is used.


Version 1.0 32

Table 2.14: Speed-Capacity Table Using Link Class

Class Speed Capacity Class Speed Capacity 100 0 0 700 0 0 101 55 1,850 701 23 550 102 57 1,900 702 30 600 103 60 1,900 703 32 650 104 60 1,900 704 38 700 105 64 2,000 705 40 800 200 0 0 800 0 0 201 48 1,200 801 20 400 202 51 1,250 802 23 425 203 55 1,300 803 26 450 204 57 1,400 804 32 475 205 62 1,500 805 33 500 300 0 0 900 0 0 301 32 900 901 40 1,500 302 39 950 902 40 1,550 303 41 1,000 903 40 1,600 304 47 1,100 904 45 1,650 305 52 1,150 905 45 1,700 400 0 0 1000 0 0 401 29 850 1001 25 800 402 32 900 1002 30 900 403 36 950 1003 30 900 404 43 1,000 1004 35 1,000 405 45 1,050 1005 35 1,000 500 0 0 1100 0 0 501 28 800 1101 17 9,999 502 34 850 1102 22 9,999 503 38 900 1103 27 9,999 504 42 950 1104 31 9,999 505 45 1,000 1105 35 9,999 600 0 0 1200 0 0 601 27 700 1201 20 9,999 602 30 750 1202 25 9,999 603 34 800 1203 35 9,999 604 41 850 1204 45 9,999 605 44 900 1205 55 9,999

Speed is in mph, capacity is in vehicles per hour per lane Note: CLASS 301 indicates Area Type 1 for Facility Type 3

Similarly, CLASS 1205 indicates Area Type 5 and Facility Type 12

Note: An adjustment factor, calibrated during assignment validation, is applied to the above speed and capacity values for some jurisdictions south of James River.


Version 1.0 33

3 Trip Generation

The trip generation process is the first step in a traditional four-step modeling process. The trip generation module estimates the total amount of trip making occurring within each TAZ. Trip generation is influenced by the input land use factors such as households, number of autos per household and employment. As a part of the trip generation process in Hampton Roads model, trip productions and trip attractions are separately estimated for each TAZ. A production is a trip end that is associated with the traveler’s home, while an attraction is a trip end that is not associated with the traveler’s home (such as a workplace or retail store).

The trip generation model includes Internal-Internal (I-I) trips, Internal-External (I-E) trips, External-Internal (E-I) trips, and External-External (E-E) trips. The NHTS was used as the source of data for the I-I and I-E trips that are produced by residents of the modeling area. Trip rates are determined for six trip purposes – Home Based Work (HBW), Home Based Shop (HBS), Home Based Social/Recreation (HBSR), Home Based Other (HBO), Non Home Based Work (NHBW), and Non Home Based Other (NHBO). A home-based trip is one for which one end of the trip is the traveler’s home. The non-home based trips are generated into two categories, NHBW and NHBO. An NHBW trip is one where neither end is the traveler’s home. An NHBO trip is part of a tour that does not include a stop at the traveler’s workplace. After the trip generation phase, the trips are then combined into four main purposes namely HBW, HBS, HBO (HBO+HBSR) and NHB (NHBW+NHBO). The trip production rates include I-I and I-E trips. The trip attraction rates for the I-I trips are determined from the NHTS dataset for HBW, HBS, HBO and NHB. I-E and E-I trips are estimated using relationships derived from other models (eg: Allentown, PA, Baltimore and Atlanta) and data on external station counts.

Trip generation is influenced by socioeconomic variables such as income levels, auto-ownership levels, etc. In the Hampton Roads model, a household stratification model is used to estimate the joint distribution of households by size and auto-ownership. This model was calibrated from NHTS data. . The stratified percentages of households are then applied to the actual households by TAZ from the land use data. This process creates a cross classification for each TAZ of households by persons (1, 2, 3, 4+) and by autos (0, 1, 2, 3+). The household stratification is an indicator of the socio-economic characteristics of a modeling area and is an input to the trip generation process to estimate the trip productions. Table 3.1 shows the seed table derived from NHTS data used in household stratification. It indicates the proportion of households with 4 categories of household size and 4 categories of auto ownership levels.

Table 3.1: Seed Table for Household Stratification

Persons/ HH

Autos/HH 0 1 2 3+

1 0.062 0.157 0.042 0.013 2 0.013 0.065 0.165 0.072 3 0.003 0.032 0.068 0.077

4+ 0.010 0.024 0.096 0.101


Version 1.0 34

Table 3.2 and Table 3.3 show the stratified percentages of households for the Hampton Roads modeling area.

Table 3.2: Percent Household Distribution by Household Size

HH Size

% 1-Person

% 2-Persons

% 3-Persons

% 4+-Persons

1.0 100.0 0.0 0.0 0.0 1.1 95.0 3.1 1.2 0.7 1.2 89.7 6.2 3.3 0.8 1.3 83.7 8.8 6.4 1.1 1.4 78.3 11.9 7.6 2.2 1.5 72.6 15.5 8.4 3.5 1.6 66.7 19.0 9.7 4.6 1.7 60.0 24.0 10.2 5.8 1.8 54.1 28.2 10.8 6.9 1.9 49.4 30.4 11.7 8.5 2.0 42.5 35.8 12.2 9.5 2.1 38.6 37.4 12.4 11.6 2.2 34.9 38.4 12.8 13.9 2.3 31.5 38.8 13.7 16.0 2.4 28.1 39.0 14.5 18.4 2.5 26.3 37.8 14.9 21.0 2.6 23.8 33.6 18.6 24.0 2.7 22.7 32.2 19.7 25.4 2.8 21.0 28.0 24.0 27.0 2.9 19.1 25.8 26.1 29.0 3.0 17.8 23.4 26.9 31.9 3.1 15.7 22.0 27.8 34.5 3.2 14.8 19.8 27.2 38.2 3.3 13.1 18.3 25.6 43.0 3.4 12.0 18.5 22.5 47.0 3.5 10.8 18.3 19.7 51.2 3.6 9.9 17.5 17.6 55.0 3.7 8.2 16.3 15.3 60.2 3.8 7.5 14.3 13.9 64.3 3.9 6.3 13.3 12.2 68.2 4.0 5.5 11.2 10.3 73.0 4.1 4.5 10.3 8.1 77.1 4.2 4.1 9.2 6.9 79.8 4.3 3.3 8.0 6.2 82.5 4.4 2.7 6.2 4.5 86.6 4.5 1.4 4.1 3.4 91.1 4.6 0.9 2.0 1.5 95.6 4.7 0.0 0.0 0.0 100.0


Version 1.0 35

Table 3.3: Percent Household Distribution by Auto-Ownership Level

Auto/HH % 0-Car % 1-Car % 2-Cars % 3+-Cars 0.0 100.0 0.0 0.0 0.0 0.1 93.7 4.1 2.2 0.0 0.2 86.6 8.6 4.8 0.0 0.3 79.9 13.0 7.1 0.0 0.4 72.4 17.7 9.9 0.0 0.5 65.8 22.1 12.1 0.0 0.6 59.9 25.5 14.3 0.3 0.7 52.5 28.8 18.3 0.4 0.8 46.8 32.0 20.6 0.6 0.9 41.2 34.7 22.6 1.5 1.0 34.7 37.8 25.9 1.6 1.1 30.0 39.5 27.5 3.0 1.2 23.9 42.5 30.1 3.5 1.3 18.3 45.5 31.5 4.7 1.4 13.5 46.1 35.0 5.4 1.5 11.6 42.4 39.0 7.0 1.6 9.3 38.2 44.7 7.8 1.7 6.8 34.2 50.4 8.6 1.8 5.9 30.7 51.8 11.6 1.9 4.8 30.1 48.8 16.3 2.0 4.2 27.4 48.0 20.4 2.1 3.8 24.7 46.9 24.6 2.2 3.0 22.8 45.3 28.9 2.3 2.9 20.3 43.1 33.7 2.4 2.4 19.4 39.0 39.2 2.5 2.1 18.1 35.4 44.4 2.6 2.0 16.3 32.0 49.7 2.7 1.6 14.4 29.5 54.5 2.8 1.4 12.9 25.8 59.9 2.9 1.3 10.8 23.0 64.9 3.0 1.0 10.6 17.4 71.0 3.1 1.0 8.4 14.5 76.1 3.2 0.8 5.8 12.6 80.8 3.3 0.5 5.5 7.0 87.0 3.4 0.0 2.6 5.0 92.4


Version 1.0 36

Table 3.4 shows the NHTS versus model household stratifications.

Table 3.4: Household Stratifications by Household Size and Auto Ownership

NHTS

Person/HH Autos/HH 0 1 2 3+ Total

1 37,874 94,832 25,778 7,988 166,473 2 8,191 39,685 100,000 43,862 191,738 3 1,579 19,445 41,719 47,153 109,896

4+ 5,886 14,870 58,619 61,727 141,102 Total 53,530 168,832 226,116 160,731 609,209

Avg Persons/HH 2.53 Avg Auto/HH 1.94

Model

Persons/HH Autos/HH 0 1 2 3+ Total

1 28,571 80,321 26,520 20,018 155,430 2 9,547 38,919 93,285 48,641 190,392 3 2,786 22,737 42,014 45,930 113,467

4+ 8,982 18,121 59,466 61,043 147,612 Total 49,886 160,098 221,285 175,632 606,902

Avg. Persons/HH 2.59 Avg. Autos/HH 2.01

Figure 3.1 and Figure 3.2 show the stratification curves that show the distribution of households by auto ownership and household size. The curves representing the model have similar nature as the NHTS data.


Version 1.0 37

Figure 3.1: Stratification of Households by Household Size by Auto Ownership

Figure 3.2: Stratification of Households by Auto Ownership by Household Size

010,00020,00030,00040,00050,00060,00070,00080,00090,000

100,000

0 1 2 3 4 5

HH

Autos/HH

Stratification of HH with 1 Person/HH by Auto Ownership

NHTS

Model

0

20,000

40,000

60,000

80,000

100,000

120,000

0 1 2 3 4 5

HH

Autos/HH

Stratification of HH with 2 Persons/HH by Auto Ownership

NHTS

Model

05,000

10,00015,00020,00025,00030,00035,00040,00045,00050,000

0 1 2 3 4 5

HH

Autos/HH

Stratification of HH with 3 Persons/HH by Auto Ownership

NHTS

Model

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0 1 2 3 4 5

HH

Autos/HH

Stratification of HH with 4+ Persons/HH by Auto Ownership

NHTS

Model

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 1 2 3 4 5

HH

Persons/HH

Stratification of HH with 0 Car/HH by HH Size

NHTS

Model

010,00020,00030,00040,00050,00060,00070,00080,00090,000

100,000

0 1 2 3 4 5

HH

Persons/HH

Stratification of HH with 1 Car/HH by HH Size

NHTS

Model

0

20,000

40,000

60,000

80,000

100,000

120,000

0 1 2 3 4 5

HH

Persons/HH

Stratification of HH with 2 Cars/HH by HH Size

NHTS

Model

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0 1 2 3 4 5

HH

Persons/HH

Stratification of HH with 3+ Cars/HH by HH Size

NHTS

Model


Version 1.0 38

The trip production model is a cross-classification model based on the household size and auto-ownership levels. Trip rates for calculating trip productions are stratified by household size and auto ownership level.

The trip attraction model is based on linear regression and is a function of employment and households in each TAZ. However, the NHTS data for attractions is not statistically adequate to permit the estimation of attraction rates by auto ownership level. Hence, for the purposes of trip generation, the trip productions and attractions are developed across all auto-ownership levels. Later, they are split into 0 and 1+ auto ownership levels as a part of the trip distribution process. The trip rates for productions are shown in Table 3.5 and the trip rates for attractions are shown in Table 3.6.

Table 3.5: Trip Production Rates from NHTS for I-I and I-E Trips

Purpose Person/ HH

0 auto/ HH

1 auto/ HH

2 autos/

HH

3+ autos/

HH HBW 1 0.2500 0.6400 1.0400 1.0800 HBW 2 0.5100 0.7600 1.3600 1.3600 HBW 3 0.6200 0.8400 1.9600 3.0800 HBW 4 0.7300 1.2800 1.9600 3.3000 HBS 1 0.4800 0.7700 0.8300 0.8300 HBS 2 1.5100 1.5200 1.6800 1.6800 HBS 3 1.5100 2.1800 2.1900 2.1800 HBS 4 1.8500 2.5700 3.1000 3.6200

HBSR 1 0.3000 0.3000 0.5800 0.5800 HBSR 2 0.5600 0.7700 0.7700 0.7700 HBSR 3 0.7000 1.0800 1.1300 1.7300 HBSR 4 0.9800 2.1300 2.2000 2.7600 HBO 1 0.4900 0.4900 0.6400 0.6400 HBO 2 1.0000 1.1300 1.1300 1.1300 HBO 3 2.6500 2.6500 2.9700 2.9700 HBO 4 3.2600 3.2600 5.7600 6.1000

NHBW 1 0.7000 0.9100 1.2400 1.9400 NHBW 2 0.8100 1.0600 2.6000 2.8200 NHBW 3 0.9900 1.1200 3.5500 4.3600 NHBW 4 0.9900 1.1200 3.9700 5.1300 NHBO 1 0.9800 1.0200 1.6500 1.6500 NHBO 2 1.9000 2.5700 2.5700 2.5700 NHBO 3 1.9000 3.5100 3.5900 3.5900 NHBO 4 1.9000 3.8000 5.2400 6.8200


Version 1.0 39

Table 3.6: Trip Attraction Rates from NHTS for I-I Trips

The trip attractions in NHTS dataset were analyzed in SPSS to test their statistical significance with the land use variables. Many different combinations of the available TAZ-level land use variables were tested. For testing purposes, the observed data was grouped into 50 districts as shown in Figure 3.3.

Purpose Retail Emp Non-Retail Emp Households PopulationHBW 1.154 0.477 0.000 0.000HBS 1.840 0.000 0.000 0.257HBO 0.000 0.000 2.220 0.000NHB 2.416 0.180 0.753 0.000


Version 1.0 40

Figure 3.3: Districts for Evaluating Attraction Rates

The districts were created to deal with the sparseness of the NHTS attraction-level data. Retail and non-retail employment were significant variables for HBW attractions. Retail employment was also important for HBS and NHB attractions. Non-retail employment was significant for HBW and NHB attractions. It was also seen that households and population affected the trip attractions in the Hampton Roads modeling area. The household variable was significant for HBO and NHB purposes while population was a significant variable for the HBS purpose. The final trip attraction equations are those with the highest statistical significance (overall r2), most logical variables, and most reasonable coefficient values. The equations were estimated including a constant term, which was subsequently removed by adjusting the other coefficients. The attraction models are applied at the TAZ level.


Version 1.0 41

The trip production equations and trip attraction equations are shown below.

Trip production at a TAZ = (Trip production rate for person/HH and auto/HH) x (household stratification for the TAZ)

Trip attraction at a TAZ = (Trip attraction rate for retail x retail employment) + (Trip attraction rate for non-retail x non-retail employment) + (Trip attraction rate for HH x HH) + (Trip attraction rate for population x population)

As a part of the highway assignment validation effort, adjustments were made to the trip productions by geography based on accessibility and area type of TAZs.

Accessibility is a non-dimensional statistic defined by modelers to describe the ability of people to travel to jobs. If a zone is surrounded by a lot of jobs, it is said to have high accessibility to employment. Accessibility combines the attributes of employment density with proximity to good quality roads and is defined for each zone i using the following term:

∑Ε

=j ij

ji t

A 2

where: Ai is the accessibility for zone i, Ej is the employment in zone j tij is the travel time between zones i and j.

This statistic is calculated for every zone-zone pair and summed for each zone. The resulting value has no real meaning by itself, but the higher its value, the more accessible the zone is to employment, in a general sense. Figure 3.4 shows a plot of the relative accessibility by zone.

Early versions of the model underestimated HBW attractions in zones with high accessibility and overestimated them in low-accessibility zones. Thus, an adjustment factor was developed by trial and error, calculated as 0.9 + (sqrt(accessibility)/1000). This helped correct this error and resulted in model estimates that more closely matched the NHTS attractions.


Version 1.0 42

Figure 3.4: Accessibility Values

It was discovered that in addition to an accessibility bias, the model initially had a development density bias. It was generally overestimating trip ends in the less developed areas and underestimating them in the more developed areas. This suggested another adjustment factor based on the development density of the zone and its environs, as measured by the area type code. The adjustments based on the area type of the TAZs are shown in Table 3.7.

Table 3.7: Area Type Adjustments to Trip Productions and Attractions

Area Type Code

Area Type Description

Adjustment Factor

1 CBD 1.25 2 Urban 1.16 3 Dense Suburban 1.05 4 Suburban 1.00 5 Rural 0.90


Version 1.0 43

In addition to the adjustments based on accessibility and area type, adjustments for trip productions and attractions were also done for the trips produced in or attracted to Gloucester and York counties as a part of the validation process. Although NHTS is an important dataset for modeling trip generation, the final assessments of the trip productions and attractions were done based on the observed traffic count data as a part of the highway validation process. A factor of 0.4 was used for Gloucester County and a factor of 0.85 was used for York County to adjust the trip productions and attractions from and to those jurisdictions, based largely on the highway assignment results.

The Internal-Internal (I-I) trips and Internal-External (I-E) trips for the Hampton Roads modeling area are largely estimated using NHTS data. Since NHTS is a household survey, the data gives adequate information to estimate trips produced within the modeling area. The External-Internal (E-I) trips and External-External (E-E) trips are estimated using the traffic counts at the external stations and relationships derived from other areas.

3.1 Internal-Internal Trips

I-I trips are the trips produced in internal zones and attracted to internal zones, that is, the trips produced and attracted within the modeling area. The trip productions estimated using the cross-classification model represent the total of I-I and I-E trips. For the purposes of modeling, these trips need to be split into I-I trips and I-E trips.

3.2 Internal-External Trips

I-E trips are the trips produced in internal zones and attracted to areas outside of the modeling area or the external zones. For example, a trip from home to work where home is an internal zone (in Hampton Roads) and work an external zone (outside Hampton Roads), the morning work trip from home to work and the evening trip from work to home are both considered as I-E trips. Experience from other areas, largely developed from a combination of home interview survey and roadside cordon interviews, suggests that the closer a zone is to the modeled cordon, the higher its share of external travel. Based on this, an equation was developed for estimating the I-E share of trips using a declining function of the TAZ’s distance to the nearest external station in miles. The Hampton Roads model uses two separate I-E share equations for the work and non-work purposes as shown below. The I-E shares by purpose are subtracted from the total trip productions to estimate I-I trips produced in each internal TAZ. The I-E equation was developed using the NHTS data. Since the household survey gives information about the productions at home end, the trip information about I-E travel from NHTS is considered quite reliable. The equations for work and non-work purpose are developed and calibrated based on the trip data available from NHTS.

3.3 External-Internal Trips

E-I trips are all the trips that are produced in external zones and attracted to the internal zones. E-I trips are generally made by non-residents of the modeled area. Since NHTS does not give any information about E-I trips and there is no other data source available to estimate the E-I travel patterns, the I-E trip equations are used for generating E-I trips.


Version 1.0 44

3.4 External-External Trips

External-external (E-E) trips are those that pass completely through the modeled area. These trips are difficult to estimate because there was little observed data available to describe them. Since this is a common situation, a procedure was developed to synthesize E-E trip values. Although this approach produces reasonable values, it should be revisited at such time when external survey data may become available.

The procedure is described as follows (refer to Table 3.9):

• Develop a set of E-E shares to be applied to each external station’s total volume, stratified by the facility type of the route. For passenger cars, these were borrowed from other areas (see Table 3.8). For heavy trucks, a previously developed set of E-E shares (see Table 3.9 column 5) was available from the VDOT document Hampton Roads Travel Forecasting Model, Truck Model Technical Report, June 30, 2010. The E-E share should be higher for major facility types than for minor ones, since prior surveys have shown that the E-E share of traffic is much higher on freeways than collectors, and most local roads that have no E-E trips at all.

• The E/E shares were applied to each external station’s volume by vehicle type to develop estimates of E-E trips by station. During assignment validation, it was discovered that a higher truck E-E share was necessary and so the prior values were increased by 26%. The values for a few stations were adjusted further in order to make the E-E trips balance properly. Table 3.9 shows the final values for passenger cars and heavy trucks.

• Examine the external stations and identify those station-to-station movements which are highly unlikely to occur due to geography. An example is 1479 (I-64) to 1478 (Old Stage Rd). From this, create a zone-zone matrix with 1 in the E-E cells whose movement is feasible and 0 in the others.

• Fratar this 0/1 table to match the estimated E-E trip ends by vehicle type (Auto/Medium Truck vs. Heavy Truck).

• Certain “through route” E-E movements can be expected to be more prevalent than others. For example, the movement between I-64 and the Outer Banks is a major trip pattern along the east coast. These movements should be factored upwards to emphasize them and thus increase their volume.

• Re-Fratar the matrix to match the original E-E trip ends. • Balance the matrix (add the matrix and its transpose and divide by 2) so that the trip

movements are symmetrical (on a daily basis) and round each cell to integer values.


Version 1.0 45

Table 3.8: Passenger Car E-E Shares

Facility Type E-E Share Interstate 20% Minor Freeway 19% Principal Arterial 16% Major Arterial 10% Minor Arterial 7% Major Collector 1% Minor Collector 0% Local 0%

Table 3.9: Cordon Travel

Station Route

Pass. Car Total

Heavy Truck Total

VDOT Truck E-E Share

Pass. Car EE Trip Ends

Truck E-E Trip Ends

Pass. Car External

Truck External

1461 Chesapeake Bay BT (US 13) 7,717 406 21.2% 2,932 326 4,785 80 1462 Princess Anne Rd (615) 3,652 152 14.2% 512 54 3,140 98 1463 Blackwater Rd (911) 846 44 9.5% 0 10 846 34 1464 Chesapeake Expwy (168) 21,273 434 17.4% 8,084 287 13,189 147 1465 Geo Wash Hwy S (US 17) 11,605 740 11.2% 3,714 210 7,891 530 1466 Desert Rd (604) 283 0 0.0% 0 0 283 0 1467 Carolina Rd (32) 3,701 236 15.8% 740 94 2,961 142 1468 Adams Swamp Rd (642) 454 10 0.0% 0 0 454 10 1469 Whaleyville Blvd (US 13) 4,560 240 8.4% 912 50 3,648 190 1470 Pittmantown Rd (668) 800 376 5.8% 0 54 800 322 1471 Southampton Pkwy (US 58) 16,478 2246 11.6% 5,272 658 11,206 1,588 1472 Carrsville Hwy (258) 9,851 0 6.4% 198 0 9,653 0 1473 Blackwater Rd (603) 995 0 0.0% 20 0 975 0 1474 Windsor Blvd (US 460) 8,166 1330 10.0% 1,634 336 6,532 994 1475 Broadwater Rd (620) 1,402 105 3.1% 28 0 1,374 105 1476 Mill Swamp Rd (626) 499 43 55.6% 0 0 499 43 1477 Old State Hwy (10) 3,615 231 8.2% 506 48 3,109 183 1478 Old Stage Rd (30) 8,075 898 0.2% 162 0 7,913 898 1479 I-64 41,689 4632 7.1% 16,676 822 25,013 3,810 1480 W Richmond Rd (US 60) 6,903 214 4.0% 138 22 6,765 192 1481 John Tyler Hwy (VA 5) 3,273 66 0.0% 458 0 2,815 66 1482 Jamestown Rd (31) 1,195 0 0.0% 168 0 1,027 0

1483 John Clayton Mem Hwy (3/14) 11,082 226 6.5% 1,552 36 9,530 190

1484 Dutton 2,341 48 11.7% 328 14 2,013 34


Version 1.0 46

Station Route

Pass. Car Total

Heavy Truck Total

VDOT Truck E-E Share

Pass. Car EE Trip Ends

Truck E-E Trip Ends

Pass. Car External

Truck External

1485 S Quay Rd (189) 1,737 260 12.4% 244 82 1,493 178 1486 Geo Wash Mem Hwy (US 17) 12,154 248 0.0% 9,724 56 2,430 192

1487 Lewis B Puller Mem Hwy (VA 33) 6,194 466 0.0% 4,956 24 1,238 442

1488 Adner (VA 14) 3,895 206 N/A 546 10 3,349 196 1489 Proctors Bridge 218 14 N/A 4 0 214 14 1490 Great Fork 113 0 N/A 0 0 113 0 1491 New 460 Future External TAZ

Using the external cordon counts, the coefficients on the equations are adjusted so that the sum of the externals from the I-E, E-I and E-E trips equals the cordon totals at each external station.

I-E share for work = 0.085 * (Distance of TAZ to the nearest external station ^ -0.347) I-E share for non-work = 0.069 * (Distance of TAZ to the nearest external station ^ -0.175)

Since no better data was available the E-I share was assumed to be equal to the I-E share which is a common practice in four step travel demand modeling (e.g. MWCOG model and others)

3.5 Initial Time of Day Procedures

As noted previously, the time of day step is actually split into two calculations. The first, which splits the average weekday trips by peak and off-peak, is applied following the trip generation step. The trip distribution and mode choice models are applied separately for these two periods. After mode choice, these two periods are further divided into AM and PM peak periods and Midday and Night off-peak periods. The specific time periods are defined in Table 3.10. These periods were defined based on NHTS data as well as using the HOV operation times in the model area.

Table 3.10: Time Periods

Period Hours AM Peak (AM) 6:00 am – 9:00 am Midday (MD) 9:00 am – 3:00 pm PM Peak (PM) 3:00 pm – 6:00 pm Night (NT) 6:00 pm – 6:00 am

The peak and off-peak period factors for internal travel are determined from the NHTS dataset using the proportions of the trips in the time periods defined above. The peak and off-peak factors for external travel are determined using the observed time-of-day counts at TMS locations. The peak and off-peak factors are further adjusted as a part of the highway validation process. The factors are shown in Table 3.11.


Version 1.0 47

Table 3.11: Peak and Off-Peak Factors

Purpose NHTS Update for

Validation Peak Off-peak Peak Off-peak

HBW 0.62000 0.38000 0.56532 0.43468 HBS 0.32000 0.68000 0.29178 0.70822 HBO 0.44000 0.56000 0.40119 0.59881 NHB 0.35000 0.65000 0.31913 0.68087 External 0.41000 0.59000 0.37384 0.62616


Version 1.0 48

4 Trip Distribution

Trip distribution is the second step in a four-step modeling process. Trip distribution is the process of estimating the number of trips that will travel between each zone-to-zone pair in the region. The trip generation process estimates the number of trip ends at each TAZ whereas the trip distribution process allocates the trips between all TAZs and generates a trip table. The Hampton Roads model uses a conventional gravity model for trip distribution in both peak and off-peak periods.

4.1 Level of Service Inputs

The gravity model uses some measure of travel between each zone pair. In Hampton Roads model, the highway impedance measured in terms of the highway travel times is used as the Level-Of-Service (LOS) to estimate regional trip distribution. The minimum impedance paths are calculated using generalized cost to evaluate highway skims. The generalized cost is a function of time, toll and value of time as shown below.

Cost = Time + Toll/Value of Time

Here, Time includes the zone-zone time computed from the network plus terminal time at both ends of the trip. Terminal time is considered as the time to walk from the car or bus to the actual final destination. It is estimated using a look-up table based on the zone’s computed area type as shown in Table 4.1. Currently the toll cost is only included in the LOS impedance for trip distribution of non-work trips with optional provision to include it for work trip distribution in the future.

The highway skimming is done separately for Single Occupancy Vehicle (SOV), High Occupancy Vehicle (HOV), and Truck paths. The trip distribution process for person travel uses the SOV skimmed time as impedance measure which is common practice in four-step travel demand modeling. The first iteration of the speed feedback process uses congested skims for the peak and off-peak periods from one of the earlier full model runs. This is done so that the convergence of the modeled link speeds can occur faster. The highway skims get updated from the second iteration onwards as a part of the feedback procedure. The Peak and Off-Peak Value of Time in the above equation were assumed to be $10.00/hour (Source: South Florida Stated Preference Travel Survey and Toll Mode Choice Model, July 2006).

Table 4.1: Terminal Times

Area Type Area Type Description Time (min.)

1 CBD 6 2 Urban 6 3 Exurban 4 4 Suburban 3 5 Rural 2


Version 1.0 49

A comparison of the skimmed times from the model and real travel times for a selected pairs of locations is shown below in Table 4.2. Note that the times from Google directions are taken by roughly estimating the starting point and the ending point of the trip and they may or may not represent the actual zone locations. It is also not possible to know if the times shown in the Google directions represent peak or off-peak periods. The model accounts for the effects of congestion but the Google directions may or may not.

Table 4.2: Comparison of Travel Times in Minutes

From To Google Directions

Pk Times Model

OP Times Model

Chesapeake (Crestwood Middle School) Norfolk (MacArthur Center) 16 20.67 19.14

Newport News/Williamsburg Airport

Newport News (28th/Jefferson Ave) 24 27.28 26.91

Newport News (28th/Jefferson Ave) Naval Station Norfolk 35 60.22 48.65

Chesapeake (Crestwood Middle School)

Virginia Beach (Water Front, Atlantic Ave/Laskin Rd) 32 35.66 34.99

Virginia Beach (Water Front, Atlantic Ave/Laskin Rd)

Newport News (28th/Jefferson Ave) 47 60.10 65.88

Virginia Beach (Water Front, Atlantic Ave/Laskin Rd) Norfolk (MacArthur Center) 24 33.71 32.94

4.2 Gravity Model and Friction Factor Calibration

In addition to the highway skims, the gravity model for trip distribution uses friction factors as the inputs. Friction factors are decaying travel functions that indicate the propensity of travelers to make trips of various durations. The friction factors in the Hampton Roads model are calibrated for each of the four I-I trip purposes and for the I-E trip purpose. Thus, there are ten separate friction factor curves calibrated for five trip purposes and two time periods. The calibration is done so that the district-level trip distribution patterns, average trip lengths, and trip length frequency distributions in the model match the observed NHTS data. The observed average trip length and trip length frequencies are estimated using the NHTS data and congested highway skims from the old Hampton Roads model. The average trip length (ATL) indicates the average time in minutes and the average distance in miles. The trip length frequency distribution (TLFD) indicates the number of trips by each increment of time or distance.

The friction factors were initially developed using CUBE’s automatic gravity model calibration procedure. The congested skims from the old Hampton Roads model were used as inputs along with the NHTS trips as the targets as a starting point. The procedure iteratively processed NHTS TLFDs along with the congested skims and gave an estimate of the friction factors. However, these friction factors did not have a smooth, logical curve since the NHTS targets were not available for all time ranges. In theory, for most travel time increments, the likelihood of making a trip of x+1


Version 1.0 50

minutes should be equal to or less than the likelihood of making a trip of x minutes, because people usually want to minimize their travel time, all other things being equal. A non-linear regression process was developed to smoothen the friction factor curves for each trip purpose. The non-linear regression equation was set as follows:

Ln(FF) = Ln(A) + B * Ln(T)

where: T = time, FF=friction factor. Friction factors for the first iteration are taken from the CUBE’s gravity model calibration procedure, A, B = constants derived using a linear-regression relationship in Excel for the above equation.

The process was iteratively done until the friction factor curve appeared smooth, and the average trip lengths and trip length frequency distributions using the friction factors matched the NHTS numbers. The friction factor curves calibrated for I-I trips are shown in Figure 4.1 and Figure 4.2. The curves are plotted as friction factor in logarithmic scale versus time in minutes. The results from the trip distribution procedure are shown in Chapter 9.

Figure 4.1: Friction Factor Calibrated Curves for Peak Period

1

10

100

1,000

10,000

100,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

HBW Peak

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

HBO Peak

1

10

100

1,000

10,000

100,000

1,000,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

HBS Peak

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

NHB Peak


Version 1.0 51

Figure 4.2: Friction Factor Calibrated Curves for Off-Peak Period

4.3 Internal Trips with 0 car/HH

Stratifying home-based trips by automobile ownership is a key attribute of the best practice in advance practice four-step travel models. The 0 car/HH market is more likely to use transit and hence stratifying the trips by socioeconomic level becomes important for the transit calibration and validation processes.

Analysis of the NHTS data suggested that it would be productive to split the internal person trips into those by car-owning households vs. carless households. Since the NHTS data did not have enough information on carless households at the TAZ level, the data for observed 0 car/HH travel is developed at a district level. In order to balance the need for accuracy vs. the statistical validity of the data, 80 districts (Source: HRTPO planning districts) are created for the modeling area as shown in Figure 4.3. The districts are created to deal with the sparseness of the NHTS 0 car/HH data. The attraction splits at the district level are then applied to the TAZ level to estimate the attractions for the 0 car/HH trips. Frataring is done to balance the trips to the total trip productions with 0 car/HH. The attraction splits are further modified manually for each period and purpose until the model shows a reasonable number of 0 car/HH trips at the jurisdiction level when compared to the NHTS data. The observed NHTS data for 0 car/HH had very few records to provide accurate comparison to modeled trips. This process is done for HBW, HBS and HBO purposes. The NHB purpose does not use market segmentation by car ownership level – since neither end of the trip is the traveler’s home, the traveler’s car ownership is unknown. The 0 car/HH trips by purpose and period are exhibited in Table 4.3 through Table 4.8. The validation results for 1+ car/HH trips by purpose are shown in Chapter 9.2.

1

10

100

1,000

10,000

100,000

1,000,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

HBW Offpeak

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

1 10 19 28 37 46 55 64 73 82 91 100

109

118

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

HBO Offpeak

HBO Offpeak

110

1001,000

10,000100,000

1,000,00010,000,000

100,000,0001,000,000,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

HBS Offpeak

110

1001,000

10,000100,000

1,000,00010,000,000

100,000,0001,000,000,000

1 11 21 31 41 51 61 71 81 91 101

111

Fric

tion

Fact

or (L

og S

cale

)

Time (min)

NHB Offpeak

NHB Offpeak


Version 1.0 52

Figure 4.3: Districts for 0 car/HH analysis


Version 1.0 53

Table 4.3: Comparison of NHTS vs. Model Trips for 0 Car/HH – Peak HBW

HBW Peak NHTS

hbwpk0 Chesapk Nfolk City Ptsmouth Suffolk VA

Beach Isle_Wight N'port News

Hampton City Poquoson W'burg James

City York Cnty Gloucester Total

Chesapeake 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Norfolk City 0 1,206 0 0 0 0 0 0 0 0 0 0 0 1,206 Portsmouth 0 0 530 0 0 0 0 0 0 0 0 0 0 530 Suffolk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VA Beach 1,506 898 0 306 3,622 0 0 0 0 0 0 0 0 6,332 Isle of Wight 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Newport News 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hampton City 0 0 0 0 0 0 0 1,320 0 0 0 0 0 1,320 Poquoson 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Williamsburg 0 0 0 0 0 0 0 0 0 0 0 0 0 0 James City 0 0 0 0 0 0 0 0 0 0 0 0 0 0 York County 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gloucester 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total 1,506 2,104 530 306 3,622 0 0 1,320 0 0 0 0 0 9,389 HBW Peak Model

hbwpk0 Chesapk Nfolk City Ptsmouth Suffolk VA




Chesapeake 654 124 83 22 85 3 14 24 1 2 5 4 2 1,023 Norfolk City 1,279 1,571 394 53 800 3 42 180 1 3 7 6 3 4,344 Portsmouth 565 151 576 35 110 1 18 43 0 1 2 2 1 1,507 Suffolk 126 48 43 146 44 3 15 24 1 2 5 5 2 464 VA Beach 1,322 493 215 48 1,623 4 31 89 2 5 9 7 4 3,851 Isle of Wight 17 9 6 8 8 1 3 7 0 1 1 1 1 63 Newport News 561 198 235 74 168 3 330 793 1 3 14 6 12 2,397 Hampton City 239 101 96 33 94 2 113 960 1 2 6 4 4 1,655 Poquoson 6 4 2 2 4 0 2 7 0 0 1 1 0 28 Williamsburg 11 9 6 2 9 0 9 13 0 2 41 2 2 106 James City 61 30 23 9 32 1 17 51 0 2 54 3 4 286 York County 63 26 25 10 26 1 21 91 1 1 8 2 3 278 Gloucester 10 8 3 3 8 1 4 7 0 1 2 1 3 50 Total 4,913 2,774 1,707 443 3,010 23 620 2,289 10 25 154 44 41 16,052


Version 1.0 54

Table 4.4: Comparison of NHTS vs. Model Trips for 0 Car/HH – Offpeak HBW

HBW Off-Peak NHTS

hbwop0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Norfolk City 0 0 5,059 0 0 0 0 0 0 0 0 0 0 5,059 Portsmouth 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Suffolk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VA Beach 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Isle of Wight 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Newport News 0 0 0 0 0 0 633 0 0 0 0 0 0 633 Hampton City 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Poquoson 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Williamsburg 0 0 0 0 0 0 0 0 0 0 0 0 0 0 James City 0 0 0 0 0 0 0 0 0 0 0 0 0 0 York County 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gloucester 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total 0 0 5,059 0 0 0 633 0 0 0 0 0 0 5,692 HBW Off-Peak Model

hbwop0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 20 244 1,272 1 9 0 9 12 0 0 0 0 0 1,568 Norfolk City 10 2,582 817 2 42 0 25 62 0 0 0 0 0 3,540 Portsmouth 1 84 2,539 1 2 0 8 6 0 0 0 0 0 2,642 Suffolk 1 51 510 18 1 0 10 10 0 0 0 0 0 601 VA Beach 18 1,414 1,324 3 336 0 25 63 0 0 1 0 1 3,184 Isle of Wight 0 7 75 0 0 0 2 3 0 0 0 0 0 87 Newport News 3 521 490 4 12 1 538 369 0 0 5 1 8 1,952 Hampton City 1 281 276 1 5 0 179 640 0 0 0 0 1 1,384 Poquoson 0 4 12 0 0 0 1 3 0 0 0 0 0 21 Williamsburg 0 10 3 0 2 0 3 3 0 2 52 1 1 76 James City 1 54 36 0 2 0 10 13 0 1 67 1 1 188 York County 0 69 70 0 1 0 24 42 0 0 7 1 2 217 Gloucester 0 4 16 0 0 0 1 1 0 0 0 0 3 26 Total 56 5,327 7,441 31 412 1 835 1,227 0 4 133 3 17 15,486


Version 1.0 55

Table 4.5: Comparison of NHTS vs. Model Trips for 0 Car/HH – Peak HBS

HBS Peak NHTS

HBSpk0 Chesapk Nfolk City Ptsmouth Suffolk VA




Chesapeake 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Norfolk City 0 3,399 0 0 0 0 0 0 0 0 0 0 0 3,399 Portsmouth 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Suffolk 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VA Beach 0 0 0 0 4,172 0 0 0 0 0 0 0 0 4,172 Isle of Wight 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Newport News 0 0 0 0 0 0 0 900 0 0 0 0 0 900 Hampton City 0 0 0 0 0 0 0 269 0 0 0 0 0 269 Poquoson 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Williamsburg 0 0 0 0 0 0 0 0 0 0 0 0 0 0 James City 0 0 0 0 0 0 0 0 0 0 280 0 0 280 York County 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gloucester 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total 0 3,399 0 0 4,172 0 0 1,170 0 0 280 0 0 9,020

HBS Peak Model

HBSpk0 Chesapk Nfolk City Ptsmouth Suffolk VA




Chesapeake 130 449 38 9 213 1 11 17 0 2 5 2 2 880 Norfolk City 36 4,505 17 3 585 0 10 27 0 3 6 2 1 5,196 Portsmouth 36 383 461 23 167 1 14 18 0 1 4 2 2 1,111 Suffolk 15 109 11 110 62 3 17 23 0 3 6 3 4 366 VA Beach 38 693 11 2 2,608 0 8 17 0 3 7 3 2 3,393 Isle of Wight 3 11 2 3 7 3 7 16 0 0 1 1 1 55 Newport News 3 13 1 0 9 0 1,097 888 2 2 9 12 10 2,046 Hampton City 3 9 1 0 9 0 144 1,948 1 2 5 4 2 2,128 Poquoson 0 1 0 0 1 0 3 16 0 0 1 1 0 22 Williamsburg 0 0 0 0 0 0 0 0 0 6 110 3 0 119 James City 0 1 0 0 0 0 2 1 0 3 192 2 0 202 York County 0 2 0 0 2 0 36 128 1 1 18 4 3 195 Gloucester 0 1 0 0 1 0 2 2 0 1 1 1 13 23 Total 264 6,177 542 150 3,666 9 1,350 3,101 5 28 365 40 39 15,736


Version 1.0 56

Table 4.6: Comparison of NHTS vs. Model Trips for 0 Car/HH – Offpeak HBS

HBS Off-Peak NHTS

HBSop0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Norfolk City 0 8,565 0 0 0 0 0 0 0 0 0 0 0 8,565 Portsmouth 0 0 1,324 0 0 0 0 0 0 0 0 0 0 1,324 Suffolk 0 0 0 900 0 0 0 0 0 0 0 0 0 900 VA Beach 0 0 0 0 7,573 0 0 0 0 0 0 0 0 7,573 Isle of Wight 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Newport News 0 0 0 0 0 0 5,233 973 0 0 0 171 0 6,378 Hampton City 0 0 0 0 0 0 0 6,857 0 0 0 0 0 6,857 Poquoson 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Williamsburg 0 0 0 0 0 0 0 0 0 0 0 0 0 0 James City 0 0 0 0 0 0 0 0 0 0 280 0 0 280 York County 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gloucester 0 0 0 0 0 0 0 0 0 0 0 0 1,802 1,802 Total 0 8,565 1,324 900 7,573 0 5,233 7,831 0 0 280 171 1,802 33,678 HBS Off-Peak Model

HBSop0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 396 391 88 14 71 1 18 21 0 0 4 1 3 1,009 Norfolk City 56 5,522 56 4 215 0 36 53 0 0 7 4 2 5,956 Portsmouth 65 316 1,196 51 40 2 24 22 0 0 5 1 2 1,725 Suffolk 1 4 4 283 1 2 30 26 0 0 1 1 7 359 VA Beach 63 969 19 2 3,494 0 19 27 0 1 20 3 5 4,621 Isle of Wight 0 0 0 0 0 5 23 24 0 0 0 0 2 55 Newport News 0 0 0 0 0 0 3,971 1,760 2 0 2 14 8 5,755 Hampton City 0 0 0 0 0 0 800 5,234 0 0 0 0 0 6,035 Poquoson 0 0 0 0 0 0 10 37 1 0 0 0 0 48 Williamsburg 0 0 0 0 0 0 0 0 0 21 294 9 0 325 James City 0 0 0 0 0 0 2 0 0 5 602 3 0 612 York County 0 0 0 0 0 0 125 191 1 1 62 10 3 392 Gloucester 0 0 0 0 0 0 1 2 0 0 0 0 29 32 Total 581 7,202 1,363 354 3,820 11 5,059 7,398 5 29 995 46 62 26,924


Version 1.0 57

Table 4.7: Comparison of NHTS vs. Model Trips for 0 Car/HH – Peak HBO

HBO Peak NHTS

HBOpk0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 155 0 0 0 0 0 0 0 0 0 0 0 0 155 Norfolk City 401 9,834 0 0 2,564 0 0 0 0 0 0 0 0 12,799 Portsmouth 0 0 382 0 0 0 0 0 0 0 0 0 0 382 Suffolk 0 0 0 41 0 0 0 0 0 0 0 0 0 41 VA Beach 0 898 0 306 0 0 0 0 0 0 0 0 0 1,204 Isle of Wight 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Newport News 0 0 0 0 0 0 10,009 0 0 0 0 0 0 10,009 Hampton City 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Poquoson 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Williamsburg 0 0 0 0 0 0 0 0 0 0 0 0 0 0 James City 0 0 0 0 0 0 0 0 0 0 4,698 0 0 4,698 York County 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gloucester 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total 556 10,732 382 347 2,564 0 10,009 0 0 0 4,698 0 0 29,289 HBO Peak Model

HBOpk0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 72 373 42 18 1,841 3 475 182 2 16 64 17 9 3,115 Norfolk City 46 5,471 53 22 4,978 5 527 358 5 12 46 26 9 11,555 Portsmouth 28 467 539 29 616 3 309 142 2 7 27 13 6 2,188 Suffolk 15 60 13 75 183 4 411 119 1 15 57 14 8 974 VA Beach 37 513 22 21 13,907 5 594 284 3 26 103 28 18 15,561 Isle of Wight 3 5 1 3 13 1 279 35 0 10 41 6 2 400 Newport News 8 15 5 5 17 2 13,850 521 1 54 264 14 3 14,759 Hampton City 8 14 4 3 16 2 5,041 2,339 1 30 121 14 3 7,597 Poquoson 1 2 0 0 2 0 146 4 0 3 15 1 0 175 Williamsburg 1 1 0 0 2 0 36 2 0 493 606 48 0 1,191 James City 4 7 2 2 8 1 34 5 0 196 1,730 13 1 2,002 York County 3 6 2 2 7 1 1,501 42 0 93 290 16 1 1,962 Gloucester 2 4 1 1 5 1 183 6 0 20 76 4 11 317 Total 226 6,938 684 181 21,594 27 23,387 4,041 18 976 3,440 214 72 61,796


Version 1.0 58

Table 4.8: Comparison of NHTS vs. Model Trips for 0 Car/HH – Offpeak HBO

HBO Off-Peak NHTS

HBOop0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 0 0 0 0 155 0 0 0 0 0 0 0 0 155 Norfolk City 401 14,014 0 0 0 0 0 0 0 0 0 0 0 14,415 Portsmouth 0 0 530 0 0 0 0 0 0 0 0 0 0 530 Suffolk 0 0 0 41 0 0 0 0 0 0 0 0 0 41 VA Beach 0 5,385 0 0 1,542 0 0 0 0 0 0 0 0 6,928 Isle of Wight 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Newport News 0 0 0 0 0 0 19,515 973 0 0 0 0 0 20,488 Hampton City 0 0 0 0 0 0 0 3,914 0 0 0 0 0 3,914 Poquoson 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Williamsburg 0 0 0 0 0 0 0 0 0 0 0 0 0 0 James City 0 0 0 0 0 0 0 0 0 0 724 0 0 724 York County 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gloucester 0 0 0 0 0 0 0 0 0 0 0 0 1,092 1,092 Total 401 19,399 530 41 1,697 0 19,515 4,887 0 0 724 0 1,092 48,286 HBO Off-Peak Model

HBOop0 Chesapk Nfolk City Ptsmouth Suffolk

VA Beach Isle_Wight

N'port News


James City


Chesapeake 103 2,502 71 8 70 1 853 274 2 2 123 12 16 4,036 Norfolk City 29 21,598 48 5 161 1 1,108 438 3 2 104 20 15 23,533 Portsmouth 36 1,958 931 35 92 3 477 202 2 2 46 13 9 3,807 Suffolk 2 313 14 108 8 1 579 127 1 1 82 6 9 1,252 VA Beach 31 7,632 42 6 1,618 1 1,357 715 6 8 233 40 46 11,737 Isle of Wight 0 15 0 0 0 2 372 23 0 0 52 1 2 467 Newport News 0 3 0 0 0 0 21,141 391 0 0 117 2 1 21,654 Hampton City 0 9 0 0 0 0 8,275 1,933 1 0 115 4 2 10,338 Poquoson 0 0 0 0 0 0 208 1 0 0 3 0 0 213 Williamsburg 0 0 0 0 0 0 104 2 0 2 811 1 0 920 James City 0 0 0 0 0 0 151 0 0 0 1,375 0 0 1,528 York County 0 0 0 0 0 0 2,353 23 0 0 211 3 0 2,591 Gloucester 0 0 0 0 0 0 283 2 0 0 36 0 14 335 Total 201 34,031 1,107 163 1,950 8 37,260 4,132 16 18 3,309 102 115 82,411


Version 1.0 59

4.4 Internal-External Trips

In the NHTS data, I-E trips are defined as being produced at an internal zone and attracted to a location outside the modeled area. However, the road used to leave the modeled area (the network’s external station) is not specified in the survey. The model treats all external travel as going between internal zones and external stations. Thus, in order for the NHTS trip records to reflect travel as specified in the model, an external station must be attached to the observed trip record. A Monte Carlo simulation process was developed to estimate the most likely external station used by each trip, based on the locations of the origin and destination zones and the traffic counts at each station. The observed average trip length and trip length frequency distributions (zone to station) are estimated using this information and the congested skims from the old Hampton Roads model.

The friction factors are initially developed using Citilabs gravity model procedure for CUBE Voyager. They are further modified using the non-linear regression method as explained in Section 4.2 to calibrate against the observed average trip length and trip length frequency distribution. The friction factor curve developed for the peak period is also used in the off-peak period.

The friction factor curve calibrated for the I-E trips in peak and off-peak periods are shown in Figure 4.4. The results from the trip distribution are shown in Chapter 9, Page 88.

Figure 4.4: Friction Factor Calibrated Curves for I-E Trips

As noted above, no observed data was available specifically for E-I travel. Therefore, it was assumed that the E-I distribution of trips between internal zones and external stations would be very similar to the I-E distribution.

1

10

100

1000

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

105

109

113

117

Fri

ctio

n F

acto

rs (L

og S

cale

)

Time (min)

Friction Factors for I-E


Version 1.0 60

5 Mode Choice

This chapter explains the structure and methodology for the mode choice model. It also describes the methodology for the transit path-building procedure.

5.1 Transit Paths and Skims

The transit path-building parameters such as the in-vehicle time weight, out-of-vehicle time weight, transfer penalty, and walk speed were taken from the version of the Hampton Roads model used to develop Norfolk Tide forecasts (Source: Norfolk LRT Project Final Design Patronage Forecasting Report, 2007) and are consistent with FTA national experience. The other parameters such as walk access capture area, drive access capture area and discount on the walk access distances were developed and calibrated for the current Hampton Roads model.

Table 5.1 shows the in-vehicle weight, out-of-vehicle weights, transfer penalty, walk access and egress speeds, and walk access and drive access capture distances. The path weights reflect the way in which travelers perceive different aspects of travel time by transit. For example, walking to a bus stop or waiting for a bus is perceived as 2.5 times onerous than traveling on the bus. The parameters and weights are used to determine the shortest transit path from zone to zone. The evaluation of the transit path is done using CUBE Voyager’s “Best Path” method in PT.

Table 5.1: Model Weights for In-Vehicle and Out-of-Vehicle Times

Variables Values In-vehicle time (min) -0.0250 Out-of-vehicle time (min) -0.0625 Cost (cents) -0.0015 Number of Transfers -0.0750 Nesting coefficients Auto/transit/Fringe nest 0.5 Walk/Drive to transit nest 1.0 Drive Alone/Shared ride nest 1.0 Other Parameters Auto Operating Cost 10.5 cents/mile SR-2 Occupancy 2 SR-3+ Occupancy 3.2

Auto Parking Cost Defined at zone level

Value of Time $10/hour


Version 1.0 61

Transit skimming is done for walk to transit and drive to transit paths for both the peak and off-peak periods. Consistent with the Hampton Roads Transit New Starts model, fringe paths are used for HBW purpose in the peak period. The following skims are generated for fringe:

• Fringe parking to charter bus (shuttle) egress • Fringe parking to transit egress • Fringe parking to walk egress

Fares are coded as based on the single-trip cash fare in 2009.

A few assumptions and adjustments are done in skimming as a part of the transit validation process:

• The walk access capture distance for WATA buses is set to 1 mile as compared to 0.5 miles for the HRT buses. Additionally, a 50% discount is given to the walk distance and time for the walk access/egress times from/to WATA buses. This is done because of the larger sized zones in Williamsburg and James City that need to effectively capture the walk access movement around College of William and Mary.

• In order to achieve the above, a different walk access mode (mode 17) is used for building walk paths to WATA instead of the regular walk access/egress mode 16.

• The WATA buses are coded as all-stop bus routes in order for the model to facilitate the access, egress and transfers effectively.

The transit path-building parameters and fare assumptions were further modified in the final implementation of the HR model to reflect the changes from HRT Virginia Beach Transit Extension Study (VBTES). Source: VBTES Calibration and Validation of the Ridership Model Report, December 2012

Transit times in the network are calculated as a function of the network highway times and are calibrated to match end to end schedule run times on buses. An adjustment to the highway times was required, as transit times need to account for the stop-and-go conditions and for the acceleration and deceleration of the buses approaching and departing each bus stop. Transit times in the network are calculated using delay functions based on the area type and facility type of the links.

Transit time = Highway time + Distance x Delay in minutes/mile

The delay is expressed in minutes per mile. For example, a bus traveling in the CBD area on an arterial has more delay due to stop-and-go conditions, and a bus traveling on a freeway in a rural area with no stops has less or no delay. The delay in minute/mile by facility and area types were created as part of the model calibration process. Table 5.2 shows the delay in minutes/mile by facility and area types applied in Hampton Roads model for the peak period. Table 5.3 shows the delay in minute/mile for the off-peak period.


Version 1.0 62

Table 5.2: Delay by Facility and Area Type in Minutes/Mile in Peak

Facility Type

CBD (1)

Urban (2)

Dense Suburban

(3)

Suburban (4)

Rural (5)

Freeways (1) 0.00 0.00 0.00 0.00 0.00 Minor Freeways (2) 0.00 0.00 0.00 1.00 0.00 Principal Arterial (3) 2.00 2.00 2.00 2.50 2.50 Major Arterial (4) 2.00 2.00 2.00 2.00 2.00 Minor Arterial (5) 3.00 0.00 2.00 3.00 2.00 Major Collector (6) 2.00 2.00 0.30 0.30 0.30 Minor Collector (7) 4.50 3.50 0.30 2.00 0.30 Local Roads (8) 2.00 2.00 0.30 0.30 0.30 High Speed Ramp(9) 0.00 0.00 0.00 0.00 0.00 Low Speed Ramp (10) 0.00 0.00 0.00 0.00 0.00 Centroid Connector (11) 0.00 0.00 0.00 0.00 0.00 Ext Sta. Connector (12) 0.00 0.00 0.00 0.00 0.00

Table 5.3: Delay by Facility and Area Type in Minutes/Mile in Off-Peak

Facility Type

CBD (1)

Urban (2)

Dense Suburban

(3)

Suburban (4)

Rural (5)

Freeways (1) 0.00 0.00 0.00 0.00 0.00 Minor Freeways (2) 0.00 0.00 0.00 1.00 0.00 Principal Arterial (3) 2.00 2.00 2.00 2.50 2.00 Major Arterial (4) 2.00 2.00 2.00 2.00 2.00 Minor Arterial (5) 3.00 0.50 2.00 2.00 2.00 Major Collector (6) 2.00 2.00 0.30 0.30 0.30 Minor Collector (7) 4.50 3.50 0.30 2.00 0.30 Local Roads (8) 2.00 2.00 0.30 0.30 0.30 High Speed Ramp(9) 0.00 0.00 0.00 0.00 0.00 Low Speed Ramp (10) 0.00 0.00 0.00 0.00 0.00 Centroid Connector (11) 0.00 0.00 0.00 0.00 0.00 Ext Sta. Connector (12) 0.00 0.00 0.00 0.00 0.00

The peak and off-peak modeled versus scheduled (observed) run times on buses are compared in Figure 5.1. The R-square for the scheduled versus modeled run times is about 0.8 for peak and about 0.75 for off-peak. This shows that the modeled times are close to the schedule times. Note


Version 1.0 63

that this analysis included bus routes representative of different regions in the modeling area (example: routes through Norfolk, Newport News, Chesapeake, etc).

Figure 5.1: Observed vs. Modeled End-to-End Bus Run Times

5.2 Mode Choice Model Structure

The mode choice models subdivide the total person trip tables from the trip distribution model into separate trip tables for each travel mode by trip purpose. The share attracted to each mode is based on the travel characteristics of competing highway and transit services, and the household automobile ownership of the traveler.

The proportion of trips selecting each mode is estimated using a logit function that relates the probability of selecting a mode to the relative utility of that mode compared to that of all other modes. The form of this function is as follows:

where: Pg,i is the probability of a traveler from group g choosing mode i; xg,i are the attributes of mode i that describe its attractiveness to group g; and Ug,m(xg,m) is the utility (or attractiveness) of mode m for travelers in group g.

The mode choice model for Hampton Roads is based on the nested logit form of this function which allows for sub-modal trade-offs to be more sensitive to service measures than higher level choices of the “main” modes. Separate models have been developed for each time period (peak and off-peak) and for each trip purpose by auto ownership. The choice set is depicted in Figure 5.2.

R² = 0.8043

0.0

20.0

40.0

60.0

80.0

100.0

120.0

0 20 40 60 80 100 120 140

Mod

eled

(min

)

Observed (min)

Schedule vs. Modeled End-End Bus Run Times - Peak

R² = 0.7579

0.010.020.030.040.050.060.070.080.090.0

100.0

0 20 40 60 80 100 120 140

Mod

eled

(min

)

Observed (min)

Schedule vs. Modeled End-End Bus Run Times - Offpeak

ee=P )]x(U[

)]x(U[

ig,mg,mg,

ig,ig,

∑


Version 1.0 64

Figure 5.2: Mode Choice Nesting Structure

In the final implementation of the HR model the TIDE LRT was coded as a local bus with a separate mode number (Mode=11) without change to the mode choice nesting structure. A path favoring IVTT discount was given to LRT mode (Source: VBTES Calibration and Validation of the Ridership Model Report, December 2012)

The relative attractiveness (or “Utility”) of each travel mode takes the following form:

where:

Ug,m(xg,m) is the utility (or attractiveness) of mode m for travelers in group g LOSm is a variable set describing levels-of-service by mode m; SEg is a variable set describing the socioeconomic characteristics of group g; TRIP is a variable set describing the characteristics of the trip; bm is vector of coefficients describing the importance of each LOSm variable; cg,m is vector of coefficients describing the importance of each SEg characteristic of group g with respect to mode m dm is vector of coefficients describing the importance of each TRIP characteristic of with respect to mode m, and am is a constant specific to mode m.

The utility of each mode is based on the weighted average of the utilities of each sub-mode (shown in the utility diagram). Ultimately the utilities of each sub-mode are defined as a function of travel times and costs depicted in the basic utility equations. The contribution of each sub-mode’s utility to

TRIPd+SEc+LOSb+a=)x(U mgmg,mmmmg,mg,


Version 1.0 65

the “full” mode utility is determined by the overall utility of each mode, which incorporates a relative contribution measure or nesting coefficient for each sub-mode. More details about the calibrated mode choice model are presented in the next section.

The Hampton Roads mode choice model is implemented using CUBE’s XCHOICE module. A total of 10 mode choice models were developed for all combinations of auto ownership, purpose and time period. Note that Home Based Other and Home Based Shopping trips were combined and share the same mode choice model as they both behave similarly with respect to mode choice. Mode choice is carried out for I-I trips only.

5.3 Mode Choice Model Calibration

The mode choice model outlined in Section 5.2 was calibrated to year 2009 conditions using auto/transit level of service data and 2009 modal usage targets by mode. Calibration involves adjusting the constants in the utility equations iteratively until the output trip totals by mode match the target values. This process is repeated for all the mode choice models. Table 5.4 shows the coefficients that define the utility functions for the Hampton Roads mode choice model. The coefficients on in-vehicle time, out-of-vehicle time and cost are consistent with FTA guidance. To ensure consistent paths between the transit path builder and mode choice, the same variable inter-relationships were used in both steps, e.g., out-of-vehicle time is weighted 2.5 times the in-vehicle time in both path builder and mode choice. The toll cost is added to the auto operating cost and divided by the auto occupancy. The coefficients do not change for the final implementation of the HR model with HRT changes.

Table 5.4: Mode Choice Model Coefficients

Variables Values Equivalent IVTT

In-vehicle time (min) -0.0250 1.00 Out-of-vehicle time (min) -0.0625 2.50 Cost (cents) -0.0015 0.06 Number of Transfers -0.0750 3.00 Nesting coefficients Auto/transit/Fringe nest 0.5 - Walk/Drive to transit nest 1.0 - Drive Alone/Shared ride nest 1.0 - Other Parameters Auto Operating Cost 10.5 cents/mile - SR-2 Occupancy 2 - SR-3+ Occupancy 3.2 - Auto Parking Cost Defined at zone

level

Value of Time $10/hour


Version 1.0 66

The mode choice constants were calibrated using the McFadden nesting structure where constants for each sub-mode in the utility function were aggregated from bottom-up multiplied by the nesting coefficient. The results from the calibration including the mode choice constants are presented in Chapter 9.


Version 1.0 67

6 Trip Assignment

This chapter describes various elements associated with the highway assignment and transit assignment procedures. The highway assignment procedure includes the time-of-day procedure, the volume-delay functions and the tolls.

6.1 Highway Assignment

Highway Assignment refers to the routing of auto trips estimated in the mode choice step along with vehicle trips such as externals and trucks. This step is performed twice in the model. The first application of highway assignment is done to get better estimates of congested travel times in the model region than the initial estimates developed using the speed-capacity table. The second application of highway assignment is done in order to load the trips onto the network and estimate the volume of vehicles (and other statistics) for each link.

This process was refined in the current version of the Hampton Roads model by dividing the entire day into four time periods, i.e., AM peak, Midday off-peak, PM peak and Night off-peak, and developing separate highway assignments for each of those time periods. The peak and off-peak trip tables are split into four time periods using the time of day factors. As mentioned in Section 3.5, Page 43, the time of day factors are determined from NHTS dataset. As a part of the highway assignment validation, the time of day factors are updated as shown below in Table 6.1. The trips from the mode choice model are in production-attraction (P-A) format and the highway assignment requires the trips to be in origin-destination (O-D) format. The factors shown in Table 6.2 derived from NHTS data are applied by purpose and time period for the P-A to O-D conversion. For example, HBW peak trips in P-A format from the mode choice model are converted to O-D format for the AM and PM periods as follows:

AM HBW O-D = [(HBW Peak P-A * 0.518) * 0.9937] + [Transpose of (HBW Peak P-A * 0.518) * (1-0.9937)]

Table 6.1: Time of Day Factors

Purpose NHTS Updated for Validation

Peak Period Off-Peak Period Peak Period Off-Peak Period AM PM Midday Night AM PM Midday Night

HBW 0.550 0.450 0.510 0.490 0.518 0.482 0.444 0.556 HBO 0.390 0.610 0.540 0.460 0.412 0.588 0.474 0.526 NHB 0.270 0.730 0.750 0.250 0.364 0.636 0.697 0.303 Ext 0.420 0.580 0.580 0.420 0.442 0.558 0.514 0.486


Version 1.0 68

Table 6.2: Factors for P-A to O-D Conversion

Period Direction HBW HBO NHB AM P -> A 99.37% 86.38% 50.00% AM A -> P 0.63% 13.62% 50.00% PM P -> A 7.65% 31.76% 50.00% PM A -> P 92.35% 68.24% 50.00% Midday P -> A 64.43% 55.82% 50.00% Midday A -> P 35.57% 44.18% 50.00% Night P -> A 37.51% 42.43% 50.00% Night A -> P 62.49% 57.57% 50.00%

The highway assignments estimate the travel on roadway facilities for the vehicles. Hence, the drive-alone, shared ride 2 and shared ride 3+ trips from the mode choice outputs are converted to vehicle trips using vehicle occupancy factors. The vehicle occupancy factor is assumed as 1.0 for drive alone trips, 2 for shared ride 2 trips and 3.2 for the shared ride 3+ trips. An average vehicle occupancy of 2.581 for the external trips was derived from the NHTS dataset.

The trip generation, trip distribution, initial time of day, and mode choice procedures estimate trip tables in Production-Attraction (P-A) format. This means that the trips are stored in a zone-zone matrix based on the row of the production zone and the column of the attraction zone. For example, a round trip to/from work where the worker lives in zone 1 and works in zone 23 is stored as 2 trips in the row 1/column 23 cell of the trip table. For the purposes of trip assignment, the trips must be converted to O-D format, which shows the actual direction of travel. In this example, that would put 1 trip in cell 1,23 and 1 trip in cell 23,1 on a daily basis. Hence, the output trip tables from the mode choice and the external trip tables in P-A format are converted to O-D format using directional factors (tabbed from NHTS) for inbound and outbound trips before assigning them to the network. Basically, this conversion involves transposing the P-A table, factoring the P-A version, factoring the A-P version, and summing the factored tables.

Each time of day has a capacity factor associated with it. The capacity factor is applied to the link capacity of the lanes to define the full capacity of the facilities in a particular time period. The basis for the capacity factor is the 15-minute count data by time periods. For each 15-minute count in a particular time period, the ratio of the sum of counts in that particular time period to 4 times the count was calculated. Thus, there were as many probable capacity factors in a time period as the number of 15-minute counts in that time period. The capacity factors were then tested during the validation process to see which of them gave decent volume/count ratios by time period. Finally, the four capacity factors as shown in Table 6.3 were used in the model.


Version 1.0 69

Table 6.3: Capacity Factors by Time of Day

Time of Day

Capacity Factor

AM 2.6 Midday 5.0 PM 2.9 Night 4.3

The results from the four highway assignments were evaluated using 2009 traffic count data of the VDOT’s Traffic Monitoring System (TMS) throughout the model region. More details regarding the highway assignment validation are presented in Chapter 9, Page 88.

6.2 Volume Delay Functions

The Volume Delay Functions (VDFs) are used to simulate the degradation of highway speeds as modeled volumes approach capacity. The VDFs employed in the highway assignment process determine the change in travel speed as a function of the Volume/Capacity (V/C) ratio. This calculation occurs in each iteration of the highway assignment until the convergence criteria are met and optimal path routing is identified given the impacts of congestion from vehicle trips loaded on the networks. In Hampton Roads model, the user-equilibrium model of traffic assignment is applied with the following default convergence criteria:

RELATIVEGAP = 0.010 MAXITERS = 50

A test run was done with more stringent criteria with Relative Gap of 0.001 and Maximum Iterations of 100 to check the highway assignment results against the usual model run. No significant differences in the highway assignments were seen in the test run. However, the model run time increased to about 9 hours compared to the 3 hours for a regular model run. In order to expedite the model run times, Relative Gap of 0.01 and Maximum Iterations of 50 were used as the default convergence criteria.

The VDFs used in the current Hampton Roads model were built on the VDF optimization research done at the Virginia Modeling, Analysis and Simulation Center (VMASC) at Old Dominion University (Source: Evaluation of Volume-Delay Functions and Their Implementation in VDOT Travel Demand Models, May 2011). Conical functions were developed for different groups of facility types such that the resulting highway link volumes matched well with the observed traffic counts. According to VMASC’s research, conical functions provide better %RMSE than BPR functions. Table 6.4 summarizes the VDFs used in the current Hampton Roads Model. The equation shown below represents the conical function:


Version 1.0 70

[ ]βαβα −−−+−+= )1(*)1(*2* 2220 VCVCTTC

where: Tc = congested time for next iteration, and T0 = time

Table 6.4: VDF Values

Facilities Alpha Beta Freeways 9.0 1.06 Minor Freeways/Principal Arterials 7.0 1.08 Major/Minor Arterials, Major Collectors 4.5 1.14 Minor Collectors/Locals 2.0 1.50

Figure 6.1 shows the variation in congested speeds with respect to free flow speeds for various Volume/Capacity ratios.

Figure 6.1: Volume Delay Function Curve

0.000

0.200

0.400

0.600

0.800

1.000

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00V/C Ratio (Level of Service E Capacity)

Volume-Delay Functions

Freeways

Minor Freeways/Principal Arterials

Major/Minor Arterials, Major Collectors

Minor Collectors/Locals

Con

gest

ed S

peed

/ Fr

ee S

peed


Version 1.0 71

6.3 Toll procedures

Tolls in the Hampton Roads region are fixed tolls collected at two locations in the base year (2009) model – George P. Coleman Bridge (Highway 17) and Chesapeake Expressway. All trips that use the above two facilities are charged a fixed toll in the highway assignment process. Table 6.5 summarizes the tolls coded in the model. Note that the regular toll on Chesapeake Expressway is $3 for the autos (2-axle) and $4 for the trucks (3-axle). They are “cash” tolls designed to extract revenue from travelers heading to the beach in the Outer Banks, North Carolina. The average weekday travelers use EZ-Pass and pay the membership fees. The discounted toll on the expressway comes at 75 cents for autos and $2 for the trucks. These toll values are used in the model. They also serve in validation to better match the observed counts on the facility.

Table 6.5: Auto and Truck Tolls

The fixed toll cost was converted to time and added to the congested time to arrive at composite time impedance, which was used to determine the best path for all trips. A peak and off-peak value of time (VOT) of $10/hr was assumed for Hampton Roads based on the experience with toll models across the country (Source: South Florida Stated Preference Travel Survey and Toll Mode Choice Model, July 2006). The VOT is multiplied by adjustment factors at each toll location which were calibrated during highway assignment.

6.4 Transit Assignment

Separate assignments are performed for the peak and off-peak periods and within each period, separate assignments are done for walk to transit and drive to transit. The assignment procedures use the trip tables generated as outputs from the mode choice procedure. For the purposes of transit assignment, the trip purposes are combined. Additionally, transit assignments for fringe-walk, fringe-transit and fringe-shuttle are done for the peak period. The results from the transit assignments are shown in Chapter 9, Page 88.

Toll Facility Auto Toll ($) Truck Toll ($)George P. Coleman Bridge (Northbound only) 2 3Chesapeake Expressway 0.75 2


Version 1.0 72

7 Heavy Truck Model

Given the growing importance of trucks and their disproportionate effect on congestion, toll revenues, and other impacts, it was judged necessary to develop a revised model to estimate Truck volumes. In this model, “Truck” means heavy trucks, defined as those with three or more axles or pulling a trailer. In the FHWA standard classification scheme, this is classes 6 through 13, inclusive (see Figure 7.1). In the VDOT classification scheme, this is vehicle classes 4-6.

Figure 7.1: FHWA Vehicle Classification System

CLASS 1: Motorcycles -- All two or three-wheeled motorized vehicles. Typical vehicles in this category have saddle type seats and are steered by handlebars rather than steering wheels. This category includes motorcycles, motor scooters, mopeds, motor-powered bicycles, and three-wheel motorcycles.

CLASS 2: Passenger Cars -- All sedans, coupes, and station wagons manufactured primarily for the purpose of carrying passengers and including those passenger cars pulling recreational or other light trailers.

CLASS 3: Other Two-Axle, Four-Tire Single Unit Vehicles -- All two-axle, four-tire vehicles, other than passenger cars. Included in this classification are pickups, panels, vans, and other vehicles such as campers, motor homes, ambulances, hearses, carryalls, and minibuses. Other two-axle, four-tire single-unit vehicles pulling recreational or other light trailers are included in this classification.

CLASS 4: Buses -- All vehicles manufactured as traditional passenger-carrying buses with two axles and six tires or three or more axles. This category includes only traditional buses (including school buses) functioning as passenger-carrying vehicles. Modified buses should be considered to be a truck and should be appropriately classified.

CLASS 5: Two-Axle, Six-Tire, Single-Unit Trucks -- All vehicles on a single frame including trucks, camping and recreational vehicles, motor homes, etc., with two axles and dual rear wheels.

CLASS 6: Three-Axle Single-Unit Trucks -- All vehicles on a single frame including trucks, camping and recreational vehicles, motor homes, etc., with three axles.

CLASS 7: Four or More Axle Single-Unit Trucks -- All trucks on a single frame with four or more axles.

CLASS 8: Four or Fewer Axle Single-Trailer Trucks -- All vehicles with four or fewer axles consisting of two units, one of which is a tractor or straight truck power unit.

CLASS 9: Five-Axle Single-Trailer Trucks -- All five-axle vehicles consisting of two units, one of which is a tractor or straight truck power unit.


Version 1.0 73

CLASS 10: Six or More Axle Single-Trailer Trucks -- All vehicles with six or more axles consisting of two units, one of which is a tractor or straight truck power unit.

CLASS 11: Five or fewer Axle Multi-Trailer Trucks -- All vehicles with five or fewer axles consisting of three or more units, one of which is a tractor or straight truck power unit.

CLASS 12: Six-Axle Multi-Trailer Trucks -- All six-axle vehicles consisting of three or more units, one of which is a tractor or straight truck power unit.

CLASS 13: Seven or More Axle Multi-Trailer Trucks -- All vehicles with seven or more axles consisting of three or more units, one of which is a tractor or straight truck power unit.

Source: Traffic Monitoring Guide - May 1, 2001; Section 4: Vehicle Classification Monitoring, http://www.fhwa.dot.gov/ohim/tmguide/tmg4.htm#tab4a1

The principal challenge in developing a Truck model is that usable survey data almost never exists. Most truck surveys are too small, collect data that is not relevant to travel demand modeling, or have results with so much variability as to be useless for model development. The best source of observed data on truck travel is traffic counts, specifically classification counts that identify truck volumes based on vehicle length and/or number of axles. VDOT provided a fairly large number of such counts statewide in its 2009 TMS database. This data includes the annual average weekday traffic count (AAWDT) and the percentage of vehicles by class. The relevant field names in the VDOT count database are PERCENTTRUCK3AXLE (also called PERCENTT_1), PERCENTTRUCK1TRAIL (PERCENTT_2), and PERCENTTRUCK2TRAIL (PERCENTT_3). An indicator of the quality of the count is also provided; this study used counts of quality “F” or higher. The three percentages were summed and multiplied by the AAWDT to determine the daily truck count. Very low counts (less than 10 vehicles per day) were dropped. The counts were transferred from the TMS database to the network via the common TMS_ID field. The total daily count in both directions was posted. These values were carefully examined to eliminate counts that were duplicative, illogical, or inconsistent with network topology. In addition, VDOT provided a separate multi-year database of hourly counts that included a “HEAVY_VEHI” field. This data was used to calculate the percentage of Trucks by the model’s four time periods and those were used to split the daily values by period and direction. Detailed examination of the hourly count data indicated that the daily sum of the hourly counts was inconsistent with the daily count totals (the hourly counts had a significantly lower daily total). Therefore, the hourly counts were used only to validate the truck time of day model.

7.1 Trip Generation

The zonal land use data provided by the HRTPO split employment into two categories: Retail and Non-Retail. Most current Truck models require finer detail on the employment data, because different types of workers generate Truck travel at very different rates. A commonly used stratification is: Retail, Office, Industrial, and Other. VDOT obtained detailed breakdowns of employment by zone, stratified by the North American Industrial Classification System, 2007 (NAICS, www.census.gov/eos/www/naics/) from a private vendor. The employment data by NAICS category was allocated to the above four groups as shown in Table 7.1.

http://www.fhwa.dot.gov/ohim/tmguide/tmg4.htm#tab4a1


Version 1.0 74

This data was used to split the HRTPO’s Non-Retail employment category into Industrial, Office, and Other by zone. The result is a more detailed split of employment that uses the NAICS information but is consistent with the HRTPO’s data.

Table 7.1: Employment Allocation by NAICS

Group 2-Digit NAICS Retail Office Industrial Other

Agriculture, Forestry, Fisheries 11 0% 0% 100% 0% Mining 21 0% 0% 100% 0% Construction 23 0% 10% 90% 0% Manufacturing 31, 32, 33 0% 10% 80% 10% Transportation 48 0% 10% 80% 10% Communications, Utilities 22 10% 10% 70% 10% Wholesale Trade 42 40% 10% 20% 30% Retail Trade 44, 45 90% 10% 0% 0% Warehousing 49 10% 20% 20% 50% Information 51 0% 60% 10% 30% Finance, Insurance, Real Estate 52, 53, 55 0% 100% 0% 0% Administration & Support 56 30% 30% 10% 30% Personal Services 72, 81 40% 30% 10% 20% Entertainment, Recreation 71 30% 10% 0% 60% Health Services 62 30% 30% 10% 30% Educational Services 61 0% 20% 0% 80% Other Professional & Related Services 54 0% 90% 0% 10% Public Administration 92 10% 40% 20% 30%

Prior truck models were researched as documented in USDOT’s Travel Model Improvement Program (TMIP) literature1. The TMIP reports highlight a truck trip generation model developed in Phoenix that uses industrial, retail, and office employment and that they considered to be a good example. The truck trip generation equation initially taken from Phoenix and calibrated for HR model is:

Truck trip ends = 0.199 * Industrial Employment + 0.141 * Retail Employment + 0.029 * Office Employment + 0.068 * HH (1)

The above Equation (1) was applied to the zonal data and adjusted to provide approximately the same number of truck trips as an earlier estimate, whose assignment produced an aggregate estimated link total that matched the aggregate count. This required an overall factor of 0.55 on the

1 NCHRP Synthesis 298, Truck Trip Generation Data, 2001)


Version 1.0 75

above equation. The trip generation model also includes area type adjustment factors as shown in Table 7.2. These factors were calibrated during truck model validation to observed counts. Except for AT 1, these factors decrease consistently from AT 5 to AT 2, suggesting that the above equation systematically overestimates Truck trips in the more developed areas.

Table 7.2: Truck Area Type Factors

Area Type Code Area Type Factor

1 CBD 1.00 2 Urban 0.68 3 Dense Suburban 0.77 4 Suburban 0.95 5 Rural 1.06

The equation estimates the number of trip productions for each zone. The number of trip attractions is set equal to the number of productions, which is the common convention for Truck models. These trip ends represent the total trip ends. In order to split these between internal and external, an external share equation was applied, similar to that used for the person trip ends. The external share (both I-E and E-I) for a zone is an exponential function of the zone’s distance to the nearest cordon station, as shown in Figure 7.2. For Trucks, the model is:

External percent = min(1.41*d-0.8, 0.95)

where: d = distance from zone to nearest external station, miles


Version 1.0 76

Figure 7.2 Truck External Share Model

This equation limits the external share to no higher than 95%. As Figure 7.2 shows, the Truck external share is higher than that for personal travel. This was found to be necessary in order for the external Truck trip ends estimated at the internal zones to match the external Truck trip ends estimated at the external stations (which were estimated as 1.0 minus the E-E share by station). For the external Truck purpose, the internal trip ends are normalized to match the external station total.

Another adjustment to the trip ends estimated by Equation (1) is a factor for truck zones. Truck zones are zones for which there is reason to believe that the rate of Truck trip ends per employee is likely to be higher than usual. This is because a review of satellite photos or local knowledge indicates the zone may contain a concentration of industrial or warehousing land uses or a specific Truck generating activity, such as a truck stop, an intermodal transfer facility, or a trucking firm office. Truck zones are specified via a 1/0 variable in the land use file: a zone either is a truck zone or it isn’t. The identification of truck zones is somewhat arbitrary. An initial list was developed by the consultant from a review of satellite photos. This was refined after a review by HRTPO staff, HRTPO’s Freight Transportation Advisory Committee, locality planners and was further refined as a result of the assignment process. Table 7.3 shows the final list of truck zones, which are mapped in Figure 7.3. For such zones, the number of trips estimated by Equation (1) is multiplied by 2.0. The final trip generation model estimated a daily total of about 63,700 Truck trips: 53,500 I-I and 10,200 external (I-E + E-I). Zone 93, 94, 462, 497 and 499 are Port zones. For these zones, the truck generation is estimated as annual TEU count number multiplied by TEU factor.

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0 5 10 15 20 25 30Distance to Cordon (mi.)

Estimated External Share

Work

Non-Work

Heavy Truck


Version 1.0 77

Table 7.3: Truck and Port Zones

Zone Name Zone Name Zone Name

14 Industrial area 370 PortIndustrial area 727 International Paper

18 Industrial area 371 Industrial area 731 Smithfield Foods

31 Lyon Shipyard 380 PortIndustrial area 839 distribution center

36 Norfolk Public Works Dept 384 tank farm 840 Target distribution center 41 Norfolk Industrial Park 385 Virginia Power 847 Bridgeway Commerce Park 59 Lambert's Point Terminal 389 Cavalier Industrial Park 865 Harbour View Commerce Park 73 Industrial area 427 Industrial area 1011 NASA Ames Research Center 74 Industrial area 428 port area 1020 Industrial park 93 NAS Norfolk 450 Industrial area 1034 Copeland Industrial Park 94 NAS Norfolk 455 Industrial area 1035 Copeland Industrial Park

139 Industrial area 457 Industrial area 1038 Industrial park 151 Norfolk Industrial Park 462 Portsmouth Marine Terminal 1047 Industrial area 170 ORF 485 Industrial area 1048 Industrial area 197 Industrial/port area 497 Industrial/port area 1112 Oakland Industrial Park 199 Industrial/port area 499 APM/Maersk 1121 NN Marine Terminal 210 Little Creek Naval Base 542 Wilroy Industrial Park 1140 Copeland Industrial Park 241 Expressway Business Park 554 Bridgeway Commerce Park 1144 NN Shipbuilding Co 318 NAS Oceana Dam Neck 559 Industrial area 1198 Ft Eustis 353 Berkley Ave Industrial Park 560 Industrial area 1334 Industrial park 357 Porttank farm 562 Lipton plant 1387 Naval Supply Depot

359 PortIndustrial area 571 Industrial area 1388 Camp Peary


Version 1.0 78

Figure 7.3: Truck Zone Map

7.2 Trip Distribution

As noted above, there is no actual observed data on the average trip length (ATL) or the trip length frequency distribution (TLFD) for Trucks. A target ATL was synthesized by analogy based on data from other models (see Table 7.5). From the NHTS data, it is possible to derive peak/off-peak ATL ratios and external/internal ATL ratios, as shown in Table 7.4.


Version 1.0 79

Table 7.4: Average Trip Length Ratios per Trip Type

I-I Trips (min)

I-I Trips (miles)

I-E Trips (min)

I-E Trips (miles)

Pk/OP Ratio

NHTS Trips

Wtd Avg

I-E/I-I Ratio

Peak HBW 26.81 10.44

25.62 8.92

1.49 556814 HBS 14.28 4.65 1.21 350301 HBO 14.98 4.94 1.15 901238 NHB 17.52 6.07 1.31 546227 18.26 1.40 Off-Peak HBW 18.04 8.68

20.11 7.74

341216 HBS 11.83 4.38 744491 HBO 13.07 5.28 1147049 NHB 13.36 5.31 1014480 13.40 1.50 Wtd Avg Pk/OP ratio: 1.36

Next, relationships were obtained from other areas to estimate a target truck ATL. Ratios of the Truck ATL to the Work (HBW) and Other (HBO) ATL were established from other models, as shown in Table 7.5.

Table 7.5: Average Trip Length Ratios for Trucks from Other Models

Area Year HBW (min)

HBO (min)

HTK (min)

HTK/ HBW

HTK/ HBO Notes

Charlotte, NC 2001 26.4 15.9 27.1 1.024 1.706 HBW, HBO weighted est avg by income; HTK is estimated

Prince William Co, VA 2005 28.0 12.5 29.1 1.037 2.330 HTK = TRK est; other values est

Baltimore, MD 2000 20.8 12.2 24.5 1.181 2.007 TRK is est; others are obs

Sioux Falls, SD 2008 15.0 10.7 10.6 0.701 0.987 all are est; HTK is TRK

Hampton Roads, VA 2009 20.7 14.8 22.5 1.091 1.519 Dominion Blvd study estimate

Averages 22.2 13.2 22.7 1.025 1.721

Hampton Roads 2009 NHTS 23.5 13.9 24.09 23.92

Avg: 24.0 (assume this is for the off-peak)


Version 1.0 80

Combining the data in Table 7.4 and Table 7.5 produces the target ATLs shown in Table 7.6.

Table 7.6: Synthesized Truck Average Trip Length

Internal (min)

External (min)

Peak 32.7 45.9 Offpeak 24.0 36.0

For example, the peak Internal value was calculated as the off-peak Internal value (24.0), multiplied by the peak/off-peak ratio (1.36).

As part of the trip distribution model, an estimate of the peak/off-peak split was made, based on data from other areas (See Table 7.5). A split of 32% peak / 68% off-peak was implemented within the trip distribution step.

A common way of developing friction factors (F factors) is to initially assume that the F’s for all time periods is 1, apply the distribution model, and examine the resulting estimated ATL and TLFD. Comparing the estimated and target ATLs suggests specific changes to the assumed F’s for testing in the next run. This process continues in iterative fashion until the estimated and target ATLs are sufficiently close and other trip distribution measures are reasonable, including the share of intrazonal trips, error in the attraction estimates by zone, and the number of gravity model iterations required. This was done separately by trip type: internal peak, internal off-peak, external peak, and external off-peak.

Figure 7.4 shows the resulting F factor curves (the peak and off-peak External F’s are the same curve). A gamma function was used to represent the F factors, because this has been shown to produce appropriate values and is simpler to calibrate. The gamma function is as follows:

GtB etF ××Α=

where: F = F factor (dimensionless) t = highway travel time, minutes A,B,G = calibrated coefficients

Table 7.7 lists the parameters for the curves in Figure 7.4.


Version 1.0 81

Figure 7.4: Truck F Factors

Table 7.7: Truck F Factor Gamma Coefficients

Trip Type A B G Internal Peak 180,000 1.000 -0.073 Internal Off-Peak 180,000 0.993 -0.130 External Peak 180,000 -0.001 -0.060 External Off-Peak 180,000 -0.001 -0.060

F factor values are estimated by minute for travel times from 1 to 120 minutes. Over 120 minutes, the F factor is defined as zero so that no trips are estimated. F values are expressed to a precision of two places to the right of the decimal point. The gravity model is set to iterate until the root-mean-square error (RMSE) on zonal attractions for all purposes is 1 or lower, or until 40 iterations have been performed, whichever occurs first.

Table 7.8 shows the final ATL comparisons. The internal trips are shown to match the target values very well. However, after considerable testing, it was concluded that the target External ATLs shown in Table 7.6 are probably not accurate. Those ATLs are based on relationships from other areas, which have a different geography than the Hampton Roads region. In Hampton Roads, most of the external trips go to/from the Richmond area and there is a very long distance from the Richmond-area external stations to downtown Norfolk, the naval bases, and the ports. Model testing disclosed that if the External F’s were adjusted to try to match the synthesized ATLs, the gravity model would not converge sufficiently on the attractions. That is, the gravity model’s estimate of


Version 1.0 82

attractions by zone did not match the generation model’s estimate of attractions by zone very well. As a result, the final estimated External ATLs are much higher than the values shown in Table 7.6.

Table 7.8: Truck ATL Comparisons

Target (min)

Estimated (min)

Internal Peak 32.7 32.0 Offpeak 24.0 23.3 External Peak 45.9 57.5 Offpeak 36.0 59.9

7.3 Time of Day

As with the other trip purposes, the Truck time of day (ToD) model is split into two parts. The first part, described above, splits the daily trips into peak and off-peak and is applied as part of the trip distribution model. The basic split is 32% peak, 68% off-peak. The second part is applied after the trip distribution model and splits the peak trips into AM and PM peak period trips and splits the off-peak trips into MD and NT period trips.

The detailed splits use a simple set of percentages to split the 2-period trip tables into four periods. Since Truck trips are estimated in O/D format, this is a straightforward calculation without the need for matrix transposition. The detailed split percentages were initially based on a consensus of heavy truck ToD models from Washington, Baltimore, and Atlanta. These percentages were subsequently modified in response to a comparison of estimated vs. counted trips by link from the assignment phase. Table 7.9 shows the final detailed ToD splits. This says, for example, that the AM peak trips are 50% of the peak total.

Table 7.9: Truck Detailed Time of Day Splits

Period Percentage AM (6:00 am to 9:00 am) 50 PM (3:00 pm to 6:00 pm) 50 MD (9:00 am to 3:00 pm) 80 NT (6:00 pm to 6:00 am) 20

7.4 Truck Assignment

Truck trips are assigned to the network along with the auto trips. They are maintained as a separate trip type through the assignment step and are output as a separate volume on each link.


Version 1.0 83

During assignment, a volume/capacity (V/C) ratio is computed for each link at the end of each assignment iteration. This ratio is used with the volume/delay function to estimate the travel time for each link for the next iteration. In the calculation of this V/C ratio, the volume is computed as the auto volume plus the Truck volume multiplied by the passenger car equivalence (PCE) factor. This factor accounts for the fact that in a traffic stream, a heavy truck uses up more roadway capacity than does a passenger car. A lot of Trucks can make a roadway seem more congested than the actual V/C ratio would suggest. Research from Baltimore suggests that a heavy truck takes up 1.5 to 3.0 times more space and capacity on a road than an auto. For this model, a PCE factor of 2.0 is used. This affects only the V/C calculation within the assignment step. The resulting volumes output on each link represent the actual Truck volume.

Trucks are prohibited from using certain roadways. In some cases, the restriction applies only to certain hours of the day. Table 7.10 (provided by HRTPO) shows the Truck-restricted roadways, which are mapped in Figure 7.5. Most of these restrictions apply all day. For those roads with a restriction from 4 pm – 6 am, the model prohibits Trucks from using such links during the PM and NT periods, which approximately coincides with those hours. In Hampton Roads, Trucks are not permitted to use the HOV roadways.

Trucks are assigned to their own paths during assignment. The definition of path-building impedance is the same as for autos: time plus the time value of any toll plus toll gate delay. For this purpose, the value of time (VOT) is assumed to be higher than that for autos (based on the increased fuel use and the value of the driver’s salary): $35.00/hour, or 58.33 cents/minute. Toll and other roadway pricing information are provided to the assignment step in a separate file. In that toll parameter file, Trucks can be assigned a different toll than autos and the truck toll can also vary by time period.

The VOT for trucks can also be adjusted by the toll facility through VOT factor in toll file .The VOT factor for trucks was generally assumed to be 3.5 times higher than SOV and HOV2+ VOT factors for toll facilities.


Version 1.0 84

Table 7.10: Truck-Restricted Roadways

Known Restrictions due to Political DecisionsJurisdiction Route Name Location Time of Restriction

Chesapeake/Virginia Beach Elbow Road Butts Station Road to Indian River Road all timesChesapeake George Washington Hwy Cedar Road to I-64 all timesNorfolk Hampton Boulevard Redgate Avenue and International Terminal Blvd 4 pm to 6 amNorfolk Colley Avenue Colley Bay and Front Street all timesNorfolk Granby Street East Ocean View Avenue and Main Street 4 pm to 6 amNorfolk Church Street Granby St and Brambleton Avenue 4 pm to 6 amNorfolk Jamestown Cresent Hampton Boulevard and Colley Bay all timesSuffolk Nansemond Pkwy Wilroy Road and Chesapeake CL all timesSuffolk Pughsville Road Shoulders Hill Road and Chesapeake CL all timesSuffolk Town Point Road Respass Beach Road and Portsmouth CL all timesYork Richneck Road Newport News CL to Fort Eustis Blvd all times

Restrictions due to Bridge Limits

Jurisdiction Route Name Crossing

Chesapeake 22ND STREET SEABOARD AV & NS RAILWAYChesapeake BELLS MILL ROAD MILL CREEKChesapeake FENTRESS AIRFLD RD POCATY CREEKChesapeake GEO. WASHINGTON HW DISMAL SWAMP CANALChesapeake GEO. WASHINGTON HW YADKINS RD & NS RAILWAYChesapeake LAKE DRUMMOND CAWY LEAD DITCHChesapeake MILITARY HIGHWAY SOUTH BR ELIZABETH RIVERChesapeake MOUNT PLEASANT ROAD CHESAPEAKE & ALBEMARLE CANALChesapeake ROUTE 0017 DEEP CREEKIsle of Wight Carrsville Highway Rte. 632 & CSX RailwayNewport News WASHINGTON AVE. NNS & DD (PRIVATE) RWYSuffolk LAKE PRINCE DRIVE LAKE PRINCESuffolk TURLINGTON RD. BR.KILBY CREEK-SPILLWAYYork MERRIMAC TRAIL QUEENS CREEK

Other RestrictionsJurisdiction Route Name Location

York Battle Rd Old York-Hampton to GW Memorial HwyWilliamsburg John Tyler VA 199 to Jamestown RdNewport News Eastwood Warwick to ColonyYork/James City/Williamsburg Colonial National Historical Parkway (all)Newport News 41st St Roanoke to ChestnutNorfolk Robin Hood Sewells Point to Azalea GardenIsle of Wight (Smithfield Town) Main St VA 10 Bypass to Church


Version 1.0 85

Figure 7.5: Truck-Restricted Roadways


Version 1.0 86

8 Feedback

Traditional four-step travel demand models apply trip generation, trip distribution, mode choice, and trip assignment steps in a sequential and independent fashion. While the individual steps are validated to observed data, it isolates the decisions regarding origin-destination, mode and route. Therefore, there is a discrepancy between the input travel times used for trip distribution and mode choice with the travel times that result from trip assignment. This discrepancy is significant for large model regions such as Hampton Roads, which have measurable congestion during some portions of the day.

The state-of-the practice approach is to feed the travel times from the trip assignment step back into the trip distribution step and repeat the application of mode choice and trip assignment using the updated results. This process can be repeated until the travel times used to determine trip patterns during the distribution process and the resulting congested travel times from the trip assignment are approximately equivalent. This consistency is determined using predetermined criteria called “convergence criteria.” When this criterion is met, the iterative model application process is terminated.

The convergence criteria used for highway assignments is based on the difference in feedback volumes between iterations. Convergence is achieved for each of the four time periods when both the difference in VMT between the current iteration and the previous iteration is less than 5% for each of the four time periods, and the difference in feedback volumes between current iteration and the previous iteration is less than 5% for 95% of the links with volumes greater than 5,000. The Method of Successive Averages (MSA) is used in the Hampton Roads model to calculate the feedback highway volume between successive iterations of the model chain. The following is the generic formula for calculating the feedback volume.

Feedback Volume = (1-(1/Iter#)) * Feedback Volume + (1/Iter#) * FDBK_1,

where: FDBK_1 = loaded volume in the current iteration; Feedback Volume = Feedback volume to be input to the next iteration Iter# = speed feedback iteration number.

Thus, for every iteration, the feedback volume is calculated as follows:

Iteration 1: Feedback Volume = FDBK_1 Iteration 2: Feedback Volume = 1/2 * Feedback Volume + 1/3 * FDBK_1 Iteration 4: Feedback Volume = 3/4 * Feedback Volume + 1/4 * FDBK_1 ... and so on. The Feedback time for the next iteration is calculated using the Feedback volume and VDF curves described in Chapter 6.2


Version 1.0 87

Table 8.1: Feedback Convergence Criterion

Model Period Criteria Peak Percent change in VMT for the last two iterations <5%, and percentage of links

with volume > 5000 have volume difference less than 5% (with respect to previous iteration.) for both AM and PM peak

Off-Peak Percent change in VMT for the last two iterations <5%, and percentage of links with volume > 5000 have volume difference less than 5% (with respect to previous iteration.) for both Midday and Night

The convergence results from the feedback process are shown in Chapter 9, Page 88.


Version 1.0 88

9 Static Validation

Model calibration refers to the development of model parameters and coefficients. Model validation refers to the process of testing a model’s ability to replicate base year conditions and its predictive capabilities. The validation to base year conditions is called static validation and is performed by comparing simulated results to the observed data not used to develop or calibrate the model. Testing the model’s predictive capabilities is called dynamic validation and involves testing the model’s sensitivity to changes in data inputs and parameters and testing the reasonableness of future forecasts.

The Base Year 2009 calibration and validation results are presented in this chapter. The following sections contain results from different steps in the model:

Section 9.1: Trip Generation Section 9.2: Trip Distribution Section 9.3: Mode Choice Section 0: Highway Assignment Section 9.5: Speed Feedback and Volume Convergence Section 9.6: Comparison of Modeled and INRIX Speeds Section 9.7: Transit Assignment

9.1 Trip Generation

The trip generation results from the model are validated against the NHTS dataset. NHTS being a household survey provides more reliable information of home based trips than the non-home based trips. The principal results from the trip generation process are shown in Table 9.1. The estimated trips for HBW, HBS and HBO match to within 5% of the observed trips. A similar comparison cannot be made for NHB trips, because the NHTS-derived NHB trip rate was modified in order for the estimated trips to match the traffic counts. The increase in NHB travel accounts for several un-measured elements of travel, including medium trucks, light-duty commercial vehicles, and internal non-resident travel, as well as survey under-reporting of trips. This is a common phenomenon in regional travel models.

Table 9.1: Trip Generation Results

Purpose NHTS Model HBW 894,152 869,166 HBS 1,084,301 1,067,931 HBO 1,976,488 1,992,304 NHB 1,473,377 3,198,076 I-E Work 24,961 21,406 I-E Non Work 168,260 206,467 Total I-I+I-E 5,621,539 7,355,352 E-I Work - 17,542 E-I Non Work - 103,653


Version 1.0 89

9.2 Trip Distribution

The trip distribution results from the model are validated against the NHTS dataset using three measures of travel: average trip length (ATL), trip length frequency distribution (TLFD), and trip patterns by jurisdiction. The comparison of the modeled vs. NHTS ATL by time and distance is shown in Table 9.2. The model is mostly within 10% of the values from NHTS. Note that “NHTS using skims” means that these statistics were calculated using the NHTS origin-destination patterns and the origin-destination travel times as computed by the network. NHTS reported travel times were not used.

As part of the calibration, in addition to the Time and Toll Value a psychological barrier penalty was applied to the trip distribution impedance for the non-work trips crossing the three major bridges/tunnels across James River. This is implemented as a function of link distance on the bridges multiplied by a factor. The factor was calibrated to 4.2 min/mile during model calibration.

The final implementation of the trip distribution in HR model was updated to reflect the changes from HRT Virginia Beach Transit Extension Study (VBTES), factoring the peak Home-Based Work (HBW) trips in the TIDE corridor to match the Census distribution. Source: VBTES Calibration and Validation of the Ridership Model Report, December 2012

Table 9.2: Average Trip Length by Time and Distance

Purpose Avg Trip Length By Time (mins) Avg Trip Length By Distance (miles)

NHTS using skims Model NHTS using skims Model

Peak HBW 25.10 26.67 10.57 11.21 HBS 14.99 15.71 4.69 5.04 HBO 15.12 15.39 4.98 4.97 NHB 17.52 18.41 6.17 6.67 I-E 56.50 58.98 39.83 41.45 E-I - 64.16 - 41.61 Off-Peak HBW 21.66 22.09 8.84 9.53 HBS 14.18 14.88 4.44 4.75 HBO 15.93 15.56 5.44 5.34 NHB 16.32 16.57 5.42 5.88 I-E 64.08 58.69 42.79 41.93 E-I - 59.26 - 42.04

Comparisons of the modeled vs. NHTS TLFD by peak and off-peak periods are shown in Figure 9.1 and Figure 9.2. The overall pattern of the estimated TLFD curves look similar to the NHTS.


Version 1.0 90

Figure 9.1: Trip Length Frequency Distribution by Time – Peak Purposes

Figure 9.2: Trip Length Frequency Distribution by Time – Off-peak Purposes

The comparison of the modeled vs. NHTS trips by jurisdiction by purpose and period are shown in Table 9.3 through Table 9.10.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

% Tr

ips

TLF by time - Peak HBW

NHTS HBW HBW Model

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

% Tr

ips

TLF by time - Peak HBS

NHTS HBS HBS Model

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

% Tr

ips

TLF by time - Peak HBO

NHTS HBO HBO Model

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

% Tr

ips

TLF by time - Peak NHB

NHTS NHB NHB Model

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

% Tr

ips

TLF by time - Offpeak HBW

NHTS HBW HBW Model

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

% Tr

ips

TLF by time - Offpeak HBS

NHTS HBS HBS Model

0.000.050.100.150.200.250.300.350.400.450.50

% Tr

ips

TLF by time - Offpeak HBO

NHTS HBO HBO Model

0.000.050.100.150.200.250.300.350.400.450.50

% Tr

ips

TLF by time - Offpeak NHB

NHTS NHB NHB Model


Version 1.0 91

Table 9.3: Comparison of 2009 Average Weekday Model vs. NHTS Peak HBW Trips by Jurisdictions

NHTS Survey Peak HBW NHTS: Survey trips are factored up so that the total survey closely matches the total model

hbwpk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News

Hampton City Poquoson W'burg James City York Cnty Gloucester Total

Chesapeake 27,602 17,046 6,764 1,964 12,711 0 1,808 1,676 0 0 0 172 0 69,742 Norfolk City 3,316 44,813 1,445 3,015 13,693 0 112 0 0 0 0 0 0 66,392 Portsmouth 2,826 3,340 8,014 1,346 1,503 857 845 100 0 0 0 183 0 19,014 Suffolk 871 1,645 3,148 8,029 1,307 132 1,150 889 0 0 0 0 0 17,171 VA Beach 16,599 28,096 9,078 1,441 78,059 0 6,491 1,524 0 0 0 0 0 141,287 Isle of Wight 371 203 87 1,020 344 3,437 2,554 821 0 0 0 0 0 8,838 Newport News 0 5,487 99 0 7,799 0 28,656 11,521 439 1,011 478 153 0 55,644 Hampton City 511 6,677 0 1,702 810 0 12,083 27,415 0 537 2,262 2,890 0 54,886 Poquoson 0 189 0 0 0 0 1,157 417 682 0 157 501 0 3,103 Williamsburg 0 0 0 0 0 0 245 0 0 2,099 1,496 763 0 4,602 James City 0 169 0 0 0 0 4,861 2,873 0 4,160 6,204 2,888 0 21,155 York County 103 0 0 0 0 0 6,240 10,351 553 871 958 3,242 189 22,506 Gloucester 0 0 303 103 0 0 723 579 0 477 190 1,836 1,709 5,920 Total 52,198 107,665 28,939 18,620 116,226 4,425 66,923 58,164 1,675 9,155 11,744 12,628 1,898 490,259

Intradsit = 239,960

Model Peak HBW Modeled trips

hbwpk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 26,909 20,199 6,213 1,441 14,253 267 2,018 1,211 22 181 187 200 35 73,136 Norfolk City 5,288 39,757 2,260 372 13,071 103 1,232 1,162 19 142 145 169 27 63,750 Portsmouth 5,705 7,008 8,536 1,048 3,842 188 1,469 872 16 119 122 142 23 29,092 Suffolk 3,908 5,447 2,467 7,373 3,181 737 1,952 1,098 20 178 182 191 34 26,768 VA Beach 13,518 31,342 3,000 633 82,879 179 1,459 1,281 23 214 220 219 41 135,007 Isle of Wight 1,191 2,294 744 911 1,262 3,362 1,868 1,028 20 170 172 197 34 13,253 Newport News 2,082 5,211 1,392 639 2,347 465 26,878 8,171 203 1,811 1,935 3,369 341 54,843 Hampton City 1,853 6,193 1,259 545 2,690 365 10,819 17,808 178 605 643 1,162 126 44,246 Poquoson 185 583 132 59 262 40 1,091 1,175 223 114 129 387 34 4,414 Williamsburg 23 71 14 6 42 4 123 49 2 1,490 824 398 9 3,053 James City 231 737 131 57 452 41 1,184 451 14 5,840 8,076 2,710 76 19,999 York County 507 1,613 362 157 744 118 5,385 2,955 208 1,745 1,545 2,923 226 18,488 Gloucester 214 711 106 46 453 34 837 374 15 428 393 442 1,254 5,307 Total 61,614 121,166 26,615 13,288 125,479 5,903 56,315 37,634 964 13,038 14,572 12,509 2,261 491,357

Intradsit = 227,468


Version 1.0 92

Table 9.4: Comparison of 2009 Average Weekday Model vs. NHTS Peak HBS Trips by Jurisdictions

NHTS Survey Peak HBS NHTS: Survey trips are factored up so that the total survey closely matches the total model

hbspk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 23,846 3,356 2,110 1,117 4,486 0 0 0 0 60 0 0 0 34,977 Norfolk City 1,001 45,161 0 0 907 0 0 0 0 0 0 0 0 47,069 Portsmouth 3,003 257 12,182 288 122 0 0 0 0 0 0 0 0 15,852 Suffolk 455 524 89 4,509 255 159 0 0 0 0 0 0 0 5,991 VA Beach 3,060 3,146 0 0 65,038 0 1,339 0 0 0 0 0 0 72,583 Isle of Wight 2,282 0 106 497 0 2,279 918 227 0 279 0 0 0 6,589 Newport News 0 405 0 0 0 0 25,310 3,011 0 0 0 1,576 86 30,388 Hampton City 0 0 0 0 0 0 16,960 35,478 0 0 0 711 0 53,148 Poquoson 0 0 0 0 0 0 370 80 729 0 0 0 0 1,178 Williamsburg 0 0 0 0 0 0 0 0 0 1,812 675 327 0 2,814 James City 0 0 0 0 0 0 1,187 117 0 1,771 10,230 2,347 0 15,651 York County 0 372 0 0 0 0 6,822 308 1,935 539 0 10,546 0 20,522 Gloucester 129 0 0 0 0 0 850 0 0 0 0 398 2,973 4,350 Total 33,776 53,221 14,487 6,411 70,809 2,438 53,756 39,220 2,663 4,461 10,905 15,905 3,058 311,111

Intradsit = 240,091

Model Peak HBS Modeled trips

hbspk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 31,158 3,520 3,134 800 4,057 12 30 20 1 79 109 38 4 42,963 Norfolk City 2,240 36,763 335 29 5,486 4 36 34 1 52 70 30 2 45,082 Portsmouth 5,247 1,058 11,131 906 895 14 29 18 1 51 70 26 2 19,446 Suffolk 2,128 628 891 10,537 672 262 55 30 2 180 247 84 9 15,726 VA Beach 4,187 5,617 131 22 77,279 3 26 20 1 89 121 42 4 87,544 Isle of Wight 253 285 89 319 301 4,873 69 36 2 173 237 84 8 6,729 Newport News 32 73 9 4 70 2 26,031 4,442 94 1,161 1,670 2,609 26 36,225 Hampton City 36 116 9 4 93 2 4,927 20,940 168 463 609 1,066 14 28,446 Poquoson 3 8 1 0 8 0 298 219 965 81 106 592 3 2,285 Williamsburg 0 0 0 0 0 0 1 0 0 1,069 636 262 0 1,968 James City 1 1 0 0 2 0 22 1 0 2,656 7,729 1,355 0 11,768 York County 6 16 1 1 16 0 2,919 748 351 1,025 1,071 4,444 14 10,612 Gloucester 6 15 1 0 20 0 48 8 1 167 185 167 2,187 2,806 Total 45,295 48,100 15,734 12,625 88,899 5,172 34,493 26,517 1,586 7,247 12,859 10,800 2,275 311,601

Intradsit = 235,106


Version 1.0 93

Table 9.5: Comparison of 2009 Average Weekday Model vs. NHTS Peak HBO Trips by Jurisdictions

NHTS Survey Peak HBO NHTS: Survey trips are factored up so that the total survey closely matches the total model

hbopk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 86,084 9,862 6,728 1,023 3,738 0 224 342 0 0 240 0 0 108,242 Norfolk City 915 86,858 5,660 0 21,071 0 1,944 249 0 0 0 0 0 116,696 Portsmouth 5,663 1,440 21,060 329 1,495 0 1,955 1,955 0 0 0 0 0 33,898 Suffolk 449 1,741 1,602 20,826 424 341 0 161 0 0 0 0 0 25,544 VA Beach 7,447 11,850 2,225 281 203,138 0 176 383 0 0 0 0 0 225,500 Isle of Wight 0 453 108 1,014 0 11,173 395 229 376 0 169 0 0 13,918 Newport News 160 1,773 3,068 0 1,046 0 59,551 10,143 138 0 5,218 5,578 0 86,675 Hampton City 0 584 131 0 0 0 14,595 49,378 158 598 320 3,747 0 69,511 Poquoson 0 0 0 0 0 0 618 277 1,907 0 375 170 0 3,349 Williamsburg 0 0 0 0 0 0 0 152 0 2,401 647 77 0 3,277 James City 0 777 0 0 180 0 633 1,164 0 17,827 33,690 1,591 0 55,862 York County 100 462 0 0 407 0 11,489 436 1,581 939 351 31,420 0 47,185 Gloucester 0 0 0 0 0 0 97 0 0 0 0 135 8,166 8,398 Total 100,818 115,800 40,582 23,474 231,500 11,515 91,677 64,869 4,161 21,765 41,012 42,718 8,166 798,054

Intradsit = 615,654

Model Peak HBO Modeled trips

hbopk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 70,717 13,581 11,308 2,258 15,950 228 408 345 11 13 52 49 19 114,939 Norfolk City 7,121 82,180 3,732 415 17,716 90 440 510 14 9 37 57 12 112,333 Portsmouth 7,504 3,766 32,070 1,676 2,290 190 257 186 6 5 22 25 8 48,005 Suffolk 3,261 1,559 3,480 30,131 1,422 1,106 358 251 9 12 46 40 17 41,691 VA Beach 12,144 22,426 2,632 402 186,948 109 504 513 16 21 83 72 31 225,901 Isle of Wight 377 499 391 1,237 477 13,978 274 186 7 9 34 32 13 17,512 Newport News 108 326 94 46 262 34 71,729 11,908 345 176 717 3,796 105 89,645 Hampton City 84 327 69 33 223 23 13,417 55,727 323 32 123 1,612 32 72,025 Poquoson 9 32 8 4 26 3 1,002 853 3,141 7 26 1,022 9 6,141 Williamsburg 5 15 3 2 18 1 191 54 3 2,182 1,932 552 7 4,965 James City 34 95 20 11 117 7 1,131 287 18 2,199 24,296 1,490 30 29,737 York County 34 111 26 13 101 10 7,214 3,360 1,054 933 1,950 14,244 79 29,129 Gloucester 28 77 17 9 97 6 469 154 15 31 97 237 6,034 7,271 Total 101,426 124,994 53,850 36,236 225,645 15,785 97,394 74,335 4,961 5,628 29,415 23,228 6,396 799,293

Intradsit = 593,376


Version 1.0 94

Table 9.6: Comparison of 2009 Average Weekday Model vs. NHTS Peak NHB Trips by Jurisdictions

NHTS Survey Peak NHB NHTS: Survey trips are factored up so that the total survey closely matches the total model

nhbpk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 73,205 3,630 2,620 3,678 11,112 0 347 807 0 0 0 0 0 95,400 Norfolk City 16,700 92,850 12,314 2,992 29,319 195 506 747 0 0 0 568 0 156,190 Portsmouth 9,565 6,721 36,064 1,276 6,007 222 0 2,680 0 0 0 0 0 62,535 Suffolk 4,757 3,897 6,386 29,649 0 1,706 0 349 0 0 0 1,344 0 48,089 VA Beach 14,082 26,741 2,640 1,099 210,198 0 2,333 1,126 0 0 0 0 0 258,219 Isle of Wight 818 0 0 1,914 0 5,912 1,654 604 0 0 0 0 0 10,902 Newport News 1,961 618 2,944 582 2,765 0 89,588 24,369 993 0 952 12,178 0 136,948 Hampton City 807 3,113 2,257 0 446 0 33,000 76,674 1,060 0 2,971 11,718 0 132,047 Poquoson 0 0 0 0 0 0 2,627 976 3,186 0 0 671 0 7,460 Williamsburg 0 0 0 0 0 1,034 3,073 347 0 12,521 10,165 2,709 0 29,849 James City 0 0 0 0 156 0 4,211 627 0 6,879 9,650 3,784 0 25,307 York County 0 798 0 0 589 0 4,334 13,466 387 3,050 3,321 15,281 549 41,774 Gloucester 0 0 0 0 0 0 4,609 0 0 0 0 155 9,546 14,310 Total 121,895 138,368 65,225 41,190 260,592 9,069 146,282 122,772 5,626 22,450 27,059 48,407 10,095 1,019,030

Intradsit = 664,325

Model Peak NHB Modeled trips

nhbpk Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 86,638 20,961 12,947 2,954 21,115 182 437 289 12 378 533 217 45 146,707 Norfolk City 9,240 101,952 2,826 272 23,290 48 319 267 10 207 287 138 21 138,877 Portsmouth 16,102 6,381 30,833 2,914 4,093 164 305 187 8 192 274 121 23 61,596 Suffolk 7,806 4,093 4,494 29,695 3,818 1,405 548 320 14 532 748 294 62 53,830 VA Beach 19,172 30,863 1,552 265 230,162 72 440 329 13 452 638 253 54 284,266 Isle of Wight 1,403 2,147 625 1,719 1,985 14,799 565 319 14 474 666 274 55 25,044 Newport News 377 1,037 148 69 863 41 78,885 16,702 537 3,385 5,104 8,586 188 115,923 Hampton City 394 1,313 145 64 984 37 21,897 61,160 647 1,091 1,544 3,199 97 92,573 Poquoson 43 135 17 8 112 5 2,028 1,672 2,249 215 306 1,517 23 8,330 Williamsburg 2 5 0 0 5 0 21 4 0 3,333 2,134 847 1 6,354 James City 23 60 6 3 68 2 290 45 3 9,711 25,068 5,004 11 40,295 York County 98 292 36 17 257 10 11,477 4,112 1,024 3,466 3,826 11,899 115 36,628 Gloucester 94 242 25 12 276 7 521 134 13 631 771 736 6,719 10,180 Total 141,391 169,480 53,652 37,993 287,030 16,772 117,734 85,540 4,544 24,068 41,897 33,085 7,414 1,020,602

Intradsit = 683,393


Version 1.0 95

Table 9.7: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak HBW Trips by Jurisdictions

NHTS Survey Off-Peak HBW NHTS: Survey trips are factored up so that the total survey closely matches the total model

hbwop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 22,589 14,300 4,841 2,237 7,653 0 2,713 0 0 0 0 0 2,157 56,490 Norfolk City 1,964 22,104 5,950 0 10,066 0 1,570 2,968 0 0 0 0 0 44,623 Portsmouth 3,904 4,482 8,698 0 434 0 889 126 0 0 0 0 0 18,534 Suffolk 1,635 1,406 268 4,689 610 2,494 582 554 0 0 0 0 0 12,239 VA Beach 10,017 26,544 975 776 49,699 0 330 1,416 0 0 0 0 0 89,757 Isle of Wight 0 0 1,628 865 0 4,753 3,443 1,522 0 0 0 0 0 12,212 Newport News 0 2,526 0 0 610 0 19,903 11,390 0 1,282 1,694 4,594 0 41,999 Hampton City 0 2,112 0 0 1,027 0 12,598 48,559 0 0 2,868 0 0 67,164 Poquoson 0 240 0 0 0 0 487 528 0 0 0 257 0 1,513 Williamsburg 0 0 0 0 88 0 311 0 0 651 0 184 0 1,234 James City 0 0 0 0 0 0 1,603 356 0 3,776 6,615 984 0 13,333 York County 130 0 0 0 0 0 3,840 3,939 0 302 1,324 3,651 2,015 15,202 Gloucester 0 0 0 0 0 0 424 453 0 0 0 0 1,791 2,667 Total 40,238 73,715 22,361 8,568 70,188 7,247 48,693 71,812 0 6,011 12,501 9,670 5,963 376,965

Intradsit = 193,702

Model Off-Peak HBW Modeled trips

hbwop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 25,257 14,139 5,061 855 8,584 102 1,201 646 9 128 119 99 14 56,214 Norfolk City 2,945 34,879 1,220 121 8,159 29 668 687 8 85 77 77 8 48,964 Portsmouth 4,109 5,522 8,413 647 2,104 67 862 437 7 62 59 58 7 22,355 Suffolk 2,982 3,859 1,897 7,120 1,884 438 1,315 667 10 137 127 107 16 20,558 VA Beach 8,786 20,846 1,813 232 70,068 66 721 703 9 195 172 125 19 103,756 Isle of Wight 745 1,660 522 590 874 3,445 1,327 640 11 127 119 112 16 10,190 Newport News 909 4,013 657 260 1,517 174 24,195 5,467 109 1,078 1,148 2,487 139 42,153 Hampton City 1,032 5,465 560 226 2,017 123 7,333 15,913 103 314 279 606 37 34,008 Poquoson 91 524 54 22 181 15 688 1,050 266 80 73 335 14 3,392 Williamsburg 7 29 3 1 15 1 30 11 0 1,223 667 348 2 2,336 James City 93 365 45 14 209 11 377 124 4 4,906 7,006 2,186 20 15,357 York County 248 1,284 141 58 506 42 4,048 2,354 192 1,401 1,096 2,701 132 14,202 Gloucester 152 557 73 25 340 18 505 211 9 283 242 361 1,299 4,075 Total 47,355 93,142 20,458 10,171 96,456 4,532 43,269 28,910 736 10,019 11,186 9,603 1,724 377,561

Intradsit = 201,784


Version 1.0 96

Table 9.8: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak HBS Trips by Jurisdictions

NHTS Survey Off-Peak HBS NHTS: Survey trips are factored up so that the total survey closely matches the total model

hbsop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 85,324 4,311 7,561 338 11,389 0 0 0 0 69 182 62 0 109,237 Norfolk City 6,921 89,212 207 178 5,070 0 0 53 0 0 301 277 0 102,218 Portsmouth 11,504 1,803 19,198 2,104 2,684 0 0 0 61 0 0 0 0 37,355 Suffolk 4,356 0 196 12,693 0 907 0 0 0 0 0 0 0 18,151 VA Beach 7,965 8,742 1,465 1,126 192,339 0 0 0 0 0 0 0 0 211,638 Isle of Wight 2,606 0 144 6,607 0 14,263 980 186 0 0 0 0 0 24,786 Newport News 0 1,080 0 0 0 114 69,525 4,798 0 0 1,586 10,002 241 87,346 Hampton City 0 1,305 0 146 0 276 6,461 54,566 0 0 0 930 0 63,684 Poquoson 0 0 0 0 0 0 720 91 249 0 0 0 0 1,060 Williamsburg 0 0 0 0 0 0 0 0 0 3,565 687 2,451 0 6,703 James City 0 0 0 0 0 0 1,620 308 0 5,991 20,952 9,755 87 38,713 York County 0 425 0 0 0 0 14,058 724 383 516 322 22,540 0 38,967 Gloucester 147 0 0 0 0 0 682 0 0 55 0 197 14,202 15,283 Total 118,824 106,878 28,770 23,192 211,482 15,559 94,048 60,725 692 10,195 24,030 46,215 14,530 755,142

Intradsit = 598,626

Model Off-Peak HBS Modeled trips

hbsop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 78,255 8,368 6,431 1,268 9,316 15 36 22 0 207 239 84 5 104,245 Norfolk City 5,168 89,214 879 35 13,499 3 64 48 0 176 199 79 2 109,367 Portsmouth 11,329 4,070 27,764 1,574 2,037 19 39 22 0 127 146 53 3 47,183 Suffolk 4,860 1,221 2,171 26,480 1,516 449 81 38 1 511 591 209 16 38,143 VA Beach 9,196 12,945 267 19 189,289 2 32 24 0 251 288 100 5 212,417 Isle of Wight 645 590 249 733 716 11,933 110 55 2 497 576 209 17 16,331 Newport News 59 179 15 3 184 1 64,379 10,103 128 3,253 3,863 5,686 38 87,890 Hampton City 105 332 18 4 347 1 11,571 50,984 250 1,395 1,544 2,446 23 69,019 Poquoson 5 15 1 0 15 0 660 512 2,921 161 194 1,053 4 5,542 Williamsburg 0 0 0 0 0 0 0 0 0 2,813 1,341 622 0 4,776 James City 0 0 0 0 0 0 20 0 0 5,546 20,182 2,810 0 28,559 York County 10 32 2 0 34 0 6,290 1,664 522 2,465 2,134 12,573 20 25,746 Gloucester 7 9 1 0 24 0 91 10 1 384 352 349 5,572 6,800 Total 109,638 116,976 37,796 30,116 216,978 12,424 83,371 63,483 3,825 17,787 31,649 26,273 5,704 756,019

Intradsit = 582,358


Version 1.0 97

Table 9.9: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak HBO Trips by Jurisdictions

NHTS Survey Off-Peak HBO NHTS: Survey trips are factored up so that the total survey closely matches the total model

hboop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 104,073 15,718 10,041 2,163 10,147 0 193 117 0 0 282 0 0 142,733 Norfolk City 2,145 140,421 770 187 20,364 0 263 55 0 0 314 0 0 164,521 Portsmouth 9,600 2,216 34,480 4,020 984 0 1,095 1,138 0 0 543 0 0 54,078 Suffolk 1,748 593 3,430 26,780 443 1,347 0 568 0 0 0 0 0 34,908 VA Beach 13,794 40,228 2,100 303 308,651 0 2,017 449 0 204 0 0 0 367,746 Isle of Wight 0 276 422 2,938 494 17,164 1,338 2,354 0 0 534 0 0 25,519 Newport News 0 1,743 624 0 3,119 0 93,806 11,040 91 580 14,001 794 144 125,941 Hampton City 0 2,488 228 179 196 0 14,571 96,918 553 702 0 4,245 0 120,081 Poquoson 0 0 0 0 0 0 864 681 4,014 0 0 0 0 5,559 Williamsburg 0 0 0 0 0 0 0 179 0 3,201 1,074 0 0 4,453 James City 0 1,124 0 0 0 0 6,273 0 0 11,309 29,151 1,583 0 49,441 York County 0 648 1,062 0 560 0 32,902 752 2,740 1,335 695 44,032 187 84,913 Gloucester 0 0 0 0 0 0 1,727 0 0 0 0 1,175 8,368 11,271 Total 131,360 205,455 53,156 36,569 344,959 18,510 155,050 114,252 7,398 17,331 46,594 51,829 8,700 1,191,163

Intradsit = 911,059

Model Off-Peak HBO Modeled trips

hboop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 103,121 19,971 18,372 3,826 24,056 453 746 689 25 24 109 104 54 171,549 Norfolk City 10,831 121,694 5,288 767 26,493 222 965 1,055 36 20 90 143 39 167,644 Portsmouth 11,949 6,521 45,080 2,846 4,097 320 400 312 11 8 38 46 19 71,646 Suffolk 5,482 2,645 5,759 43,152 2,329 1,749 515 378 15 16 74 66 36 62,216 VA Beach 18,613 32,584 4,558 961 277,225 311 1,139 1,208 43 43 196 182 96 337,157 Isle of Wight 698 829 735 2,053 821 20,206 377 265 11 12 54 52 26 26,139 Newport News 250 853 206 109 679 86 103,708 18,938 584 243 1,102 6,839 188 133,785 Hampton City 238 916 170 77 710 59 21,271 80,325 589 55 225 2,785 64 107,485 Poquoson 21 80 16 8 62 6 1,431 1,394 4,419 10 43 1,659 15 9,165 Williamsburg 10 26 7 4 33 3 263 82 5 3,009 3,158 789 13 7,401 James City 70 161 47 31 217 22 1,588 464 31 3,614 35,636 2,432 64 44,376 York County 77 270 55 31 227 25 12,052 5,409 1,594 1,318 3,165 19,121 124 43,468 Gloucester 46 103 31 20 143 14 736 271 28 42 143 427 8,843 10,847 Total 151,406 186,653 80,322 53,885 337,092 23,476 145,191 110,790 7,392 8,415 44,033 34,644 9,580 1,192,878

Intradsit = 865,539


Version 1.0 98

Table 9.10: Comparison of 2009 Average Weekday Model vs. NHTS Off-Peak NHB Trips by Jurisdictions

NHTS Survey Off-Peak NHB NHTS: Survey trips are factored up so that the total survey closely matches the total model

nhbop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 175,489 26,997 28,005 7,743 39,300 493 728 1,484 0 0 0 0 0 280,238 Norfolk City 20,286 258,843 22,366 0 38,059 527 2,784 3,580 0 0 664 2,389 4,330 353,827 Portsmouth 21,367 14,034 82,013 1,472 10,101 0 2,242 7,607 0 0 604 772 0 140,211 Suffolk 7,928 2,926 1,862 57,216 231 2,986 0 0 0 0 0 0 0 73,149 VA Beach 25,906 32,360 5,364 1,225 459,686 574 3,288 523 0 0 0 0 0 528,926 Isle of Wight 0 278 0 3,761 0 14,188 0 554 0 1,179 856 0 0 20,816 Newport News 696 6,682 413 1,769 3,435 575 222,880 30,423 3,385 278 1,477 17,645 908 290,566 Hampton City 139 2,311 1,317 0 0 554 29,276 191,612 780 1,024 1,550 8,821 305 237,689 Poquoson 0 0 0 0 0 0 3,255 1,701 5,122 0 0 504 0 10,581 Williamsburg 0 0 0 0 0 0 3,426 0 0 26,205 11,154 3,683 122 44,590 James City 604 0 0 0 0 0 2,008 4,561 0 19,648 35,608 8,525 0 70,953 York County 0 2,389 0 0 0 0 10,038 8,167 318 11,570 4,382 58,651 0 95,515 Gloucester 0 0 0 0 0 0 1,302 0 0 626 193 1,361 23,576 27,058 Total 252,414 346,821 141,340 73,186 550,811 19,898 281,227 250,209 9,606 60,530 56,488 102,350 29,241 2,174,120

Intradsit = 1,611,089

Model Off-Peak NHB Modeled trips

nhbop Chesapk Nfolk City Ptsmouth Suffolk VA Beach Isle_Wight

N'port News


Chesapeake 198,073 42,570 23,500 3,976 42,396 170 553 377 7 511 576 257 31 312,996 Norfolk City 18,214 220,453 5,755 426 48,740 58 726 684 11 444 480 261 19 296,270 Portsmouth 28,173 18,580 69,854 3,742 9,674 156 386 229 4 223 254 122 13 131,410 Suffolk 16,043 8,840 8,899 68,992 7,119 1,681 727 401 8 800 903 385 50 114,848 VA Beach 37,024 62,876 4,080 410 498,909 65 644 522 9 711 809 357 43 606,460 Isle of Wight 3,375 4,030 2,039 3,396 3,384 33,607 906 479 11 818 926 412 51 53,434 Newport News 438 1,494 189 57 1,137 28 180,030 29,293 448 9,115 10,264 14,602 210 247,307 Hampton City 636 2,699 223 58 1,822 25 40,408 138,483 614 3,357 3,490 5,580 98 197,493 Poquoson 49 176 17 5 137 2 3,362 2,999 7,027 645 670 2,655 25 17,771 Williamsburg 0 1 0 0 1 0 19 3 0 8,194 3,620 1,710 0 13,549 James City 8 18 2 0 26 0 294 43 1 17,280 58,967 9,298 6 85,943 York County 104 379 37 10 294 6 20,596 7,398 1,472 7,592 6,954 33,164 127 78,133 Gloucester 75 160 25 7 230 4 1,027 247 16 1,677 1,547 1,536 15,162 21,711 Total 302,210 362,275 114,621 81,079 613,867 35,802 249,680 181,159 9,629 51,367 89,461 70,340 15,836 2,177,326

Intradsit = 1,530,916


Version 1.0 99

9.3 Mode Choice

The process of calibration of mode choice model involves adjusting the mode specific constants iteratively until the modeled trips match the calibration targets. The calibrated mode specific constants for all purposes and time periods are shown in Table 9.11.

Table 9.11: Mode Choice Model Constants (Initial Calibration)

HBW Peak HBO Peak NHB Peak 0-Car 1+Car 0-Car 1+Car Drive Alone -7.5000 0.0000 -1.6500 0.0000 0.0000 2 per Vehicle -3.1500 -1.3550 -0.2500 -0.1500 -0.4350 3+ per Vehicle -3.2000 -2.0250 -0.3250 -0.3450 -0.7550 Walk to Transit 3.7000 -0.3000 -0.4000 -2.1500 -6.3500 Drive to Transit -7.5000 -3.9340 -7.5000 -3.1000 - Fringe Walk -7.5000 -0.7852 - - - Fringe Transit -7.5000 -1.4779 - - - Fringe Shuttle -7.5000 -0.8061 - - -

HBW OffPeak HBO Offpeak NHB Offpeak 0-Car 1+Car 0-Car 1+Car Drive Alone -7.5000 0.0000 -1.2000 0.0000 0.0000 2 per Vehicle -2.0500 -1.1750 -0.3750 -0.2250 -0.3550 3+ per Vehicle -2.1000 -1.8000 -0.6500 -0.4850 -0.5925 Walk to Transit 3.0000 -1.3500 -0.0400 -2.3500 -6.2000 Drive to Transit -7.5000 -3.5000 -7.5000 -7.5000 - Fringe Walk - - - - - Fringe Transit - - - - - Fringe Shuttle - - - - -

Top Level Constants Note: No auto ownership constants for NHB

The mode choice constants in the final implementation of the HR model are slightly different from the above Table and reflect the changes from HRT Virginia Beach Transit Extension Study (VBTES). The changes to the mode choice model include addition of LRT mode and an external input of university trips to the Home-Based Other trip table in the TIDE corridor as shown in Table 9.12 below. Source: VBTES Calibration and Validation of the Ridership Model Report, December 2012


Version 1.0 100

Table 9.12: Mode Choice Model Constants (Final implementation of HR Model)

HBW Peak HBO Peak NHB Peak 0-Car 1+Car 0-Car 1+Car Drive Alone -99.0000 0.0000 -3.3000 0.0000 0.0000 2 per Vehicle -6.3000 -2.7000 -0.5000 -0.3000 -0.8700 3+ per Vehicle -6.4000 -4.0500 -0.6500 -0.6900 -1.5100 Walk to Transit -1.7000 -1.7000 -4.3000 -4.3000 -6.4000 Drive to Transit -99.0000 -5.8400 -99.0000 -7.1800 - Walk to LRT -2.3400 -2.3400 -3.9400 -3.9400 -3.1600 Drive to LRT -99.0000 -5.6900 -99.0000 -6.8200 - Fringe Walk -99.0000 -2.8000 - - - Fringe Transit -99.0000 -4.0000 - - - Fringe Shuttle -99.0000 -2.9000 - - -

HBW OffPeak HBO Offpeak NHB Offpeak 0-Car 1+Car 0-Car 1+Car Drive Alone -99.0000 0.0000 -2.4000 0.0000 0.0000 2 per Vehicle -4.1000 -2.3500 -0.7500 -0.4500 -0.7100 3+ per Vehicle -4.2000 -3.6000 -1.3000 -0.9700 -1.1850 Walk to Transit -1.8500 -1.8500 -4.9000 -4.9000 -7.8300 Drive to Transit -99.0000 -6.5000 -99.0000 -8.4000 - Walk to LRT -1.5200 -1.5200 -3.1300 -3.1300 -2.9200 Drive to LRT -99.0000 -6.5500 -99.0000 -7.0100 - Fringe Walk - - - - - Fringe Transit - - - - - Fringe Shuttle - - - - -

Top Level Constants Note: No auto ownership constants for NHB

Source: VBTES Calibration and Validation of the Ridership Model Report, December 2012


Version 1.0 101

Table 9.13 shows the calibrated mode specific constants expressed as equivalent minutes of in-vehicle time (compared to the drive alone mode). Note that the high values of 300 minutes’ penalty for some submodes is an asserted value (based on the experience) to prevent illogical trips, e.g., drive alone trips for 0-car households.

Table 9.13: Mode Choice Model Constants in equivalent IVTT minutes (Initial Calibration)

HBW Peak HBO Peak NHB Peak 0-Car 1+Car 0-Car 1+Car Drive Alone -300.00 0.00 -66.00 0.00 0.00 2 per Vehicle -126.00 -54.20 -10.00 -6.00 -17.40 3+ per Vehicle -128.00 -81.00 -13.00 -13.80 -30.20 Walk to Transit 148.00 -12.00 -16.00 -86.00 -254.00 Drive to Transit -300.00 -157.36 -300.00 -124.00 - Fringe Walk -300.00 -31.41 - - - Fringe Transit -300.00 -59.11 - - - Fringe Shuttle -300.00 -32.24 - - -

HBW OffPeak HBO Offpeak NHB Offpeak 0-Car 1+Car 0-Car 1+Car Drive Alone -300.00 0.00 -48.00 0.00 0.00 2 per Vehicle -82.00 -47.00 -15.00 -9.00 -14.20 3+ per Vehicle -84.00 -72.00 -26.00 -19.40 -23.70 Walk to Transit 120.00 -54.00 -1.60 -94.00 -248.00 Drive to Transit -300.00 -140.00 -300.00 -300.00 - Fringe Walk - - - - - Fringe Transit - - - - - Fringe Shuttle - - - - -


Version 1.0 102

Table 9.14 shows the summary of calibration targets and the modeled trips using calibrated mode choice models.

Table 9.14: Target Values vs. Mode Choice Model Results (Initial calibration)

Peak Offpeak

HBW HBO NHB HBW HBO NHB

0 car/HH 1+car/HH Total 0 car/HH 1+car/HH Total Total 0 car/HH 1+car/HH Total 0 car/HH 1+car/HH Total Total Drive Alone Target 0 419,724 419,724 1,804 437,953 439,757 590,453 0 312,391 312,391 10,274 864,452 874,726 1,148,648 Drive Alone Model 0 418,302 418,302 2,120 435,538 437,658 589,063 0 313,202 313,202 10,028 867,867 877,895 1,148,475 Shared Ride 2 Target 3,880 34,683 38,563 37,688 350,067 387,754 275,571 5,090 36,487 41,578 61,636 593,896 655,532 623,414 Shared Ride 2 Model 5,168 34,515 39,683 39,153 349,573 388,726 277,027 5,282 35,309 40,591 58,240 599,775 658,015 622,679 Shared Ride 3 Target 3,880 9,563 13,443 34,964 242,758 277,722 152,325 5,090 10,697 15,787 31,773 377,067 408,839 402,098 Shared Ride 3 Model 5,142 9,749 14,891 35,336 244,566 279,902 153,319 5,188 10,863 16,051 35,175 368,620 403,795 403,422 Walk Transit Target 6,890 7,430 14,320 1,951 2,752 4,703 1,725 5,079 2,507 7,586 5,874 3,279 9,153 2,350 Walk Transit Model 5,714 7,906 13,620 2,206 2,808 5,014 1,192 4,880 2,714 7,594 6,368 4,234 10,602 2,769 Drive Transit Target 0 51 51 0 25 25 0 6 23 29 0 0 0 0 Drive Transit Model 0 133 133 0 211 211 0 0 108 108 0 2 2 0 Fringe Walk Target 1,459 1,459 Fringe Walk Model 1,937 1,937 Fringe Transit Target 904 904 Fringe Transit Model 1,190 1,151 Fringe Shuttle Target 2,526 2,526 Fringe Shuttle Model 2,893 3,404


Version 1.0 103

The mode choice model results for the HR model with HRT changes are shown in Table 9.15 below.

Table 9.15: Target Values vs. Mode Choice Model Results (Final Implementation)

Peak Offpeak

HBW HBO NHB HBW HBO NHB

0 car/HH 1+car/HH Total 0 car/HH 1+car/HH Total Total 0 car/HH 1+car/HH Total 0 car/HH 1+car/HH Total Total Drive Alone Target 0 419,724 419,724 1,804 437,953 439,757 590,453 0 312,391 312,391 10,274 864,452 874,726 1,148,648 Drive Alone Model 0 435,429 435,429 1,859 436,099 437,958 588,544 0 320,380 320,380 9,999 867,749 877,748 1,148,749 Shared Ride 2 Target 3,880 34,683 38,563 37,688 350,067 387,754 275,571 5,090 36,487 41,578 61,636 593,896 655,532 623,414 Shared Ride 2 Model 2,018 35,560 37,578 37,186 349,774 386,960 276,395 6,764 35,761 42,525 59,574 599,062 658,636 622,232 Shared Ride 3 Target 3,880 9,563 13,443 34,964 242,758 277,722 152,325 5,090 10,697 15,787 31,773 377,067 408,839 402,098 Shared Ride 3 Model 5,092 9,611 14,703 33,484 244,690 278,174 152,636 6,677 10,677 17,354 35,563 368,067 403,630 402,862 Walk Transit Target 6,890 7,430 14,320 1,951 2,752 4,703 1,725 5,079 2,507 7,586 5,874 3,279 9,153 2,350 Walk Transit Model 4,765 7,104 11,869 3,209 2,056 5,265 1,880 2,948 5,692 8,640 3,947 3,163 7,110 1,895 Drive Transit Target 0 51 51 0 25 25 0 6 23 29 0 0 0 0 Drive Transit Model 138 611 749 70 132 202 0 3 213 216 32 152 184 0 Fringe Walk Target 1,459 1,459 Fringe Walk Model 1,933 1,933 Fringe Transit Target 904 904 Fringe Transit Model 904 904 Fringe Shuttle Target 2,526 2,526 Fringe Shuttle Model 2,841 2,841

Source: Model Run Supporting VBTES Calibration and Validation of the Ridership Model Report, December 2012


Version 1.0 104

9.4 Highway Assignment

Highway assignment validation is an iterative process of comparing the estimated traffic volumes to observed counts and then adjusting the network coding, the assignment parameters, and other elements of travel. The robustness of the modeled volumes is measured using several validation statistics that include volume-to-count ratio, link-level R-Square (R2) and percent root mean square error (RMSE). These statistics are computed for both Screenlines in the model as well as the entire modeled region to evaluate the accuracy of the modeled volumes. The desired validation criteria as per VDOT’s Policy and Procedures Manual are shown in Table 9.16.

Table 9.16: Validation Criteria and Targets

Criterion Target R-Square for the Model Region >0.90 % RMSE for Model Region <40% % RMSE by Functional Type:

Freeways <20% Principal Arterials <35% Minor Arterials <50%

Collectors <90% Source: VTM Policies and Procedures Manual

The actual validation process involves calibration of various model parameters and model inputs iteratively until the desired validation criteria are met. The highway assignment validation took several such iterations to achieve the target validation statistics. The changes made to improve the highway validation statistics are summarized below.

• Centroid Connectors: Centroid Connectors represent the possible loading points of trips from TAZs onto the highway network. These connectors were originally borrowed from previous version of the Hampton Roads model, which did not have a detailed micro-coded highway network. Hence, the locations of the loading points were evaluated for accuracy based on the local street network and the available observed count data and were modified wherever necessary.

• Volume-Delay Functions (VDFs): The VDFs and the grouping of facilities that used same VDFs were modified as necessary. Comparisons of observed counts and modeled volumes by facility type and by volume/capacity ratio formed the basis for the adjustments.

• Speed-Capacity Table Lookups: The starting speed-capacity table was based on speeds and capacities from the previous Hampton Roads model. Speeds were replaced with speeds from INRIX data where available. Speeds and Capacities were modified based on experience during the highway assignments validation to improve the validation statistics.


Version 1.0 105

• Time-of-Day (TOD) Factors: The initial estimate of TOD splits was derived from NHTS data. These factors were adjusted slightly during the highway validation to better match the observed counts by each time period. The NHTS and the final TOD factors were summarized earlier in Chapter 3 of this report.

• Non-Home Based Trips Adjustments: As noted above, NHB trips estimated from any home interview survey data are usually under-represented as it is difficult to capture all such trips in the surveys. So, it is a common practice to adjust these trips higher to improve the model validation. The trip rates for NHB trips were increased iteratively until the modeled volumes were comparable to the observed counts. The overall adjustment turned out to be about double the original NHB trip rates.

• Geographic Adjustments in Trip Generation: A systematic bias by area type was observed during the highway validation exercise. Modeled volumes in rural areas consistently exceeded the observed counts, while the opposite was seen in the urban areas. To counter this bias, adjustments to trip productions and attractions were developed by area type. These factors were summarized in Chapter 3 In addition to area type factors, validation statistics by jurisdiction revealed that there was significantly high travel in Gloucester County and York County and factors of 0.4 and 0.85 were applied in the trip generation step respectively for those two areas.

• Geographic Adjustments to Capacity and Free-flow Speeds: A consistent pattern was observed with freeways on the north side of James River over-assigned and freeways on the south side under-assigned. To address the issue, Speed and Capacities were modified on the south side. A factor of 1.2 was calibrated and applied to jurisdictions on the south side of James River.

The final validation results from the converged fully run Hampton Roads model run are shown in Table 9.17 through Table 9.22. Figure 9.3 shows the locations of the screen lines. The overall validation results met the criteria from VTM Policies and Procedures Manual.


Version 1.0 106

Figure 9.3: Locations of Screen Lines


DRAFT V0.26 107

Table 9.17 shows the volume/count by time of day for each set of the screen lines. The total volume/count ratios for AM, Midday, PM and Night periods are within 5%.

Table 9.17: Highway Validation at Screen Lines

Screenline Observed Total Vehicle Counts vs. Modeled Volumes (by Time of Day) Daily

Count AM

Model Vol AM

Vol/ Count

Count MD

Model Vol MD

Vol/ Count

Count PM

Model Vol PM

Vol/ Count

Count NT

Model Vol NT

Vol/ Count

Count Total

Model Vol Total

Vol/ Count

1 15,152 14,350 0.95 34,875 28,563 0.82 20,331 18,314 0.90 21,686 22,969 1.06 92,044 84,196 0.91 2 13,544 19,778 1.46 41,651 45,341 1.09 25,750 27,777 1.08 29,934 30,081 1.00 110,880 122,977 1.11 3 21,474 25,940 1.21 39,910 54,122 1.36 25,952 33,771 1.30 29,345 39,583 1.35 116,681 153,416 1.31 4 14,431 16,341 1.13 21,984 29,141 1.33 18,268 20,175 1.10 16,315 24,621 1.51 70,998 90,278 1.27 5 36,085 25,974 0.72 68,223 47,689 0.70 48,955 33,735 0.69 47,355 36,081 0.76 200,618 143,479 0.72 6 57,746 64,831 1.12 119,119 117,997 0.99 81,038 82,804 1.02 90,965 91,002 1.00 348,868 356,634 1.02 7 58,716 61,665 1.05 117,886 110,766 0.94 79,942 78,303 0.98 95,841 84,453 0.88 352,385 335,187 0.95 8 41,218 36,796 0.89 76,682 68,952 0.90 52,168 46,889 0.90 62,207 53,213 0.86 232,275 205,850 0.89 9 36,260 43,447 1.20 54,491 61,797 1.13 40,216 50,158 1.25 47,849 51,737 1.08 178,816 207,139 1.16 10 11,070 9,450 0.85 19,146 17,279 0.90 15,656 11,984 0.77 16,075 13,455 0.84 61,947 52,168 0.84 11 8,794 13,718 1.56 15,044 22,931 1.52 11,113 16,969 1.53 11,264 17,756 1.58 46,215 71,374 1.54 12 50,335 52,569 1.04 80,053 90,773 1.13 62,048 65,194 1.05 66,195 70,610 1.07 258,631 279,146 1.08 13 63,124 81,972 1.30 127,303 144,464 1.13 86,741 103,778 1.20 100,234 108,901 1.09 377,402 439,115 1.16 14 135,500 136,064 1.00 233,363 251,482 1.08 162,415 173,484 1.07 184,215 181,868 0.99 715,493 742,898 1.04 15 24,720 40,154 1.62 47,757 73,478 1.54 32,812 49,115 1.50 40,578 55,823 1.38 145,868 218,570 1.50 16 52,420 60,421 1.15 101,409 106,829 1.05 67,230 76,639 1.14 81,829 81,611 1.00 302,888 325,500 1.07 17 59,654 56,610 0.95 101,775 107,012 1.05 73,998 73,288 0.99 82,818 80,665 0.97 318,244 317,575 1.00 18 32,289 29,520 0.91 58,672 54,987 0.94 40,694 36,218 0.89 43,136 41,896 0.97 174,791 162,621 0.93 19 24,235 20,278 0.84 38,157 38,523 1.01 29,987 26,394 0.88 30,416 29,600 0.97 122,796 114,795 0.93 20 62,076 61,913 1.00 124,694 115,050 0.92 82,118 81,030 0.99 89,069 82,959 0.93 357,957 340,952 0.95 21 20,155 20,034 0.99 38,404 36,162 0.94 22,311 25,069 1.12 26,951 26,822 1.00 107,821 108,087 1.00 22 38,470 43,747 1.14 66,383 82,441 1.24 46,494 55,732 1.20 52,720 63,854 1.21 204,067 245,774 1.20 23 22,503 26,076 1.16 35,259 45,619 1.29 27,999 32,758 1.17 27,942 30,328 1.09 113,703 134,781 1.19 24 51,870 46,873 0.90 106,280 86,046 0.81 70,442 59,410 0.84 80,543 66,089 0.82 309,136 258,418 0.84 25 15,406 18,551 1.20 41,109 41,399 1.01 25,260 25,814 1.02 33,311 28,733 0.86 115,086 114,497 0.99 26 27,277 20,987 0.77 37,111 30,771 0.83 24,117 22,070 0.92 30,719 23,061 0.75 119,225 96,889 0.81 27 25,054 24,064 0.96 61,040 50,678 0.83 36,827 31,217 0.85 43,095 38,014 0.88 166,016 143,973 0.87 28 12,326 12,473 1.01 31,673 24,437 0.77 18,259 16,658 0.91 18,696 18,036 0.96 80,954 71,604 0.88

Total 1,031,904 1,084,596 1.05 1,939,453 1,984,729 1.02 1,329,143 1,374,747 1.03 1,501,304 1,493,821 1.00 5,801,805 5,937,893 1.02


DRAFT V0.26 108

Table 9.18 shows the volume/count ratios on screen lines by facility type.

Table 9.18: Highway Validation on Screen Lines using Facility Type

Facility Type Description

Observed Total Vehicle Counts vs. Modeled Volumes (by Time of Day)

Count AM

Model Vol AM

Vol/ Count

Count MD

Model Vol MD

Vol/ Count

Count PM

Model Vol PM

Vol/ Count

Count NT

Model Vol NT

Vol/ Count

Count Total

Model Vol Total

Vol/ Count

1 Interstate/Principal Freeway 398,426 421,941 1.06 688,778 734,418 1.07 459,026 517,813 1.13 563,061 571,461 1.01 2,109,291 2,245,633 1.06

2 Minor Freeway 72,341 69,110 0.96 108,742 129,138 1.19 82,572 86,496 1.05 86,824 101,784 1.17 350,478 386,528 1.10

3 Principal Arterial 216,300 254,081 1.17 435,318 465,051 1.07 298,570 326,021 1.09 328,398 343,360 1.05 1,278,586 1,388,513 1.09

4 Major Arterial 55,073 57,432 1.04 101,728 108,077 1.06 69,176 73,686 1.07 76,848 80,111 1.04 302,825 319,306 1.05

5 Minor Arterial 235,167 229,534 0.98 491,029 441,559 0.90 336,749 298,678 0.89 355,841 318,990 0.90 1,418,786 1,288,761 0.91

6 Major Collector 3,876 4,773 1.23 6,621 9,391 1.42 5,457 6,424 1.18 5,098 6,971 1.37 21,051 27,559 1.31

7 Minor Collector 50,722 47,725 0.94 107,237 97,095 0.91 77,593 65,629 0.85 85,235 71,144 0.83 320,788 281,593 0.88

All Facilities 1,031,904 1,084,596 1.05 1,939,453 1,984,729 1.02 1,329,143 1,374,747 1.03 1,501,304 1,493,821 1.00 5,801,805 5,937,893 1.02

Table 9.19 shows the % RMSE by facility type for the entire modeling region.

Table 9.19: % RMSE in Highway Validation by Facility Type

Facility Type Description Daily

%RMSE

1 Interstate/Principal Freeway 19.3 2 Minor Freeway 27.6 3 Principal Arterial 29.3 4 Major Arterial 37.8 5 Minor Arterial 40.0 6 Major Collector 75.8 7 Minor Collector 60.7 8 Local 65.1

All Facilities 39.1


Version 1.0 109

Table 9.20 shows the % RMSE for the screen lines by facility types combined together. Facility type 1 and 2 are combined as Freeways, facility type 3 and 4 are combined as Principal Arterials, facility type 5 is Minor Arterial, and facility type 6 and 7 are combined as Collectors.

Table 9.20: % RMSE by Functional Class for Screen Lines

Functional

Type

Description (Facility Types)

Daily Traffic

% RMSE

1 Freeways (1-2) 19.5 2 Principal Arterials (3-4) 29.4 3 Minor Arterials (5) 45.2 4 Collectors (6-7) 57.0

Note: Functional Type is not FEDFUNC. It is combined FACTYPE for 1-2, 3-4, 5 and 6-7

% RMSE for all Screenlines = 36.0 R-Squared for all Screenlines = 0.9

Table 9.21 shows the overall RMSE and R-square for the entire modeling region.

Table 9.21: % RMSE and R-Square for Entire Model Region

% RMSE for entire model region 39.1 R-Squared for all entire model region 0.88

Table 9.22 shows the overall RMSE and volume/count ratio for trucks in the entire modeling region.

Table 9.22: % RMSE and Volume/Count Ratio for Trucks

% RMSE for trucks 69.2 Volume / Count for Trucks 1.00


Version 1.0 110

9.5 Speed Feedback and Volume Convergence

The convergence of the Hampton Roads model base year validation run was achieved in three iterations. The final percentages of links with volumes difference less than 5% in each of iteration is shown in Table 9.23. The convergence was achieved for all four time periods in third iteration.

Table 9.23: Volume Convergence for each model Feedback Iteration

Percentage of links with volume difference less than 5% Iteration AM Peak PM Peak Mid-day Night

1 - - - - 2 73.9% 80.8% 80.3% 80.4% 3 100.0% 99.7% 97.4% 97.8%

9.6 Comparison of Model and INRIX Speeds

The AM peak period congested speeds from the model were compared with the AM peak hour observed speeds from the 2010 INRIX speed data. The comparison was done based on the average congested speeds by facility type for approximately 60 locations. Figure 9.4 shows the links in the model network that were used for the comparison. Table 9.24 shows the average congested speeds by facility type.

Figure 9.4: Links for Comparison of Modeled versus INRIX Congested Speed


Version 1.0 111

Table 9.24: Comparison of Model versus INRIX Congested Speed by Facility Type

9.7 Transit Assignment

The results of the transit assignments for HRT and WATA are shown in Table 9.25. Year 2009 National Transit Database (NTD), data available on www.ntdprogram.gov is used as a source for observed average weekday ridership on HRT routes. In case of WATA routes, the observed average weekday ridership is taken from Williamsburg Area Transit Authority Transit Development Plan Report, September 20092.

Table 9.25: Average Weekday Boardings on HRT and WATA Buses

Transit Agency Observed Model HRT 53,465* 53,544 WATA 3,240** 3,001 * 2009 NTD ** WATA Transit Development Plan Report, 2009

A comparison of peak and off-peak boardings with the weighted surveyed boardings is shown in Table 9.26. The modeled boardings are within 10-15% of the observed boardings in both the time periods. Note that the table shows the peak and off-peak comparisons for the surveyed bus routes only. The transit assignment results for the HR model implementation with HRT changes are shown in Table 9.27 below.

Note: The observed survey counts are from different sources for the transit assignment validation results shown in Table 9.26 and Table 9.27 below.

2 http://www.williamsburgtransport.com/pdf/WATA_TDP_FINAL091609.pdf

Observed INRIX Model

Factype DescriptionPeak Hour Avg.

Congested Speed (mph)

AM Peak Period Avg. Congested

Speed (mph)

1 Interstates 53 52

2 Minor Freeways 52 54

3 Principal Arterials 39 42

4 Major Arterials 42 385 Minor Arterials 45 41


Version 1.0 112

Table 9.26: Comparison of Survey versus Modeled Average Weekday Boardings on HRT Buses by Peak and Off-Peak Periods – For HRT Bus Routes Surveyed (Initial validation)

Bus Route

Peak Offpeak Bus Route

Peak Offpeak

Survey Model Survey Model

Survey Model Survey Model Chesapeake

Norfolk

6 513 559 352 200

1 1,417 1,796 1,073 956 12 208 307 141 192

2 575 1,089 495 1,256

13 593 680 489 500

3 622 932 444 1,258 15 1,544 1,952 1,380 2,394

4 118 352 124 482

44 260 293 190 268

5 123 170 72 286 57 239 310 111 443

8 891 818 771 908

58 214 221 129 121

9 294 860 201 1,116 Subtotal 3,571 4,322 2,792 4,118

11 227 128 130 126

Hampton 18 49 14 22 26 101 586 540 697 730

23 828 939 806 944

102 98 117 95 113

Subtotal 5,144 7,098 4,138 7,359 103 611 620 696 776

Virginia Beach

104 470 546 505 773

20 2,018 3,312 1,683 1,560 105 532 631 433 876

25 216 384 145 260

109 122 92 115 110

26 44 164 57 141 110 279 449 328 415

27 197 101 122 49

111 318 231 385 232

29 222 390 171 261 114 685 886 644 691

33 253 44 160 25

115 373 683 434 692

36 161 748 281 428 117 171 64 136 47

Subtotal 3,111 5,143 2,619 2,722

118 277 24 285 12

VB Wave 120 121 87 112 96

30 688 58 2,372 101

Subtotal 4,643 4,968 4,865 5,563

31 404 0 897 0 Newport News 32 82 70 189 45

64 94 6 0 2

34 29 167 30 157 106 737 700 728 759

Subtotal 1,203 297 3,488 303

107 580 734 664 760

MAX/Express Routes 112 1,168 855 1,117 994

960 103 20 45 18

116 339 101 472 114

961 385 465 231 48 119 49 33 32 33

962 69 62 11 0

Subtotal 2,967 2,429 3,013 2,662

963 32 28 0 89 Portsmouth 967 116 7 8 0

41 144 255 97 466

919 162 134 0 45 604 579 537 595

922 95 104 0

47 318 440 232 653

Subtotal 962 821 295 154 50 175 130 151 249

Total Boardings On Surveyed Routes

Subtotal 1,241 1,404 1,017 1,963 TOTAL 23,056 26,481 22,444 24,844


Version 1.0 113

Table 9.27: Comparison of Survey versus Modeled Average Weekday Boardings for HR model with HRT changes

Bus Routes Survey Model Bus Routes Survey Model

Chesapeake Norfolk 6 1,040 1,090 1 3,360 1,980

12 460 750 2 1,060 1,780 13 1,320 870 3 2,200 1,670 14 380 260 4 280 150 15 4,050 3,880 5 210 300

Subtotal 7,250 6,850 8 1,680 1,410 Hampton 9 1,080 1,270

101 1,200 1,360 11 400 110 102 260 200 17 1,080 1,200 103 1,200 1,400 18 220 230 104 990 1,150 23 1,890 1,680 105 940 560 Subtotal 13,460 11,780 109 250 140 Portsmouth 110 720 900 41 440 760 111 670 860 43 90 250 114 1,410 1,220 44 480 450 115 660 1,260 45 604 419 117 380 80 47 318 634 118 770 200 50 175 126 120 240 200 57 410 530 121 60 10 Subtotal 2,517 3,169

Subtotal 9,750 9,540 Virginia Beach Newport News 20 4,510 4,260

64 90 200 25 450 550 106 1,600 2,100 26 210 580 107 1,160 1,480 27 310 370 112 1,168 687 29 410 590 116 840 530 33 470 470 119 110 70 36 480 740

Subtotal 4,968 5,067 Subtotal 6,840 7,560

MAX/Express Routes

960 250 210

961 650 460

962 100 20

967 160 50

918 40 20

919 160 140

922 160 250

Subtotal 1,520 1,150

TOTAL 46,305 45,116

Source: VBTES Calibration and Validation of the Ridership Model Report, December 2012


Version 1.0 114

10 Dynamic Validation

Dynamic Validation involves testing the sensitivity of the model outputs to changes in data inputs and parameters. If a model is not producing reasonable results when inputs are changed, it may be an indicator of underlying problems with model assumptions or structure. Thus, by testing a model’s responsiveness to the changes in inputs, dynamic validation can reveal problems with models that are not evident from static validation.

The dynamic validation for the Hampton Roads model was carried out using the changes to the input variables and the validation evaluation measures listed in the VTM. The base year 2009 model was used and changes to the following four categories of model inputs were tested:

• Land Use • Highway Network • Travel Cost • Transit Service

The validation evaluation measures included testing the sensitivity of the model to vehicle trips, Vehicle Miles Traveled (VMT), impacts to the highway facilities, mode shares and transit ridership.

The dynamic validation runs were performed before the final implementation of the HR model with HRT changes which is also expected to generate similar results.

10.1 Sensitivity Tests with Changes in Land Use

HRTPO and VDOT identified the TAZs to test the sensitivity of the model to changes in land use. The following scenarios were tested:

• Increase in households – Increase HH by 20,000 in TAZ 1342 (Williamsburg Outlet) • Increase in population – Increase population by 20,000 in TAZ 496 (Churchland) • Increase in retail employment – Increase retail employment by 10,000 in TAZ 2

(McArthur Mall) • Decrease in non-retail employment – Decrease non-retail employment by 10,000 in TAZ

99 (Naval Base)

The dynamic validation tests using the changes in land use data showed that an increase in population, households or employment created more vehicle trips and VMT. Similarly, decrease in land use data showed a reduction in the vehicle trips and VMT given that all other inputs remained the same. Table 10.1 shows the average weekday vehicle trips and average weekday VMT for the base year 2009 (static validation) scenario and the dynamic validation scenarios.


Version 1.0 115

Table 10.1: Changes in Land Use

TAZ Vehicle Trips VMT

Static Validation - 5,413,926 36,149,968 Dynamic Validation: Add 20,000 Households to a single TAZ 1342 (Williamsburg) 5,735,862 41,722,495 Dynamic Validation: Add 20,000 Population to a single TAZ 496 (Churchland) 5,466,812 36,779,558 Dynamic Validation: Add 10,000 Retail Employment to a single TAZ 2 (MacArthur Center) 5,438,983 36,891,363 Dynamic Validation: Subtract 10,0000 Nonretail Employment from a single TAZ 99 (Naval Base) 5,413,597 36,035,303

10.2 Sensitivity Tests with Changes in Highway Network

HRTPO and VDOT identified the highway facilities to test the sensitivity of the model to changes in the highway network. The following scenarios were tested:

• Removal of a major highway link - Midtown Tunnel • Addition of a major highway link - 1 lane in each direction on I-64 between Jefferson

Blvd and Route 199 • Addition of a lane of capacity to a major highway link in both directions - 1 lane in each

direction on I-64 in Newport News and Hampton • Change of free flow speed – Increase Free flow speed on I-64 by 10 mph in each

direction in Newport News and Hampton

10.2.1 Removal of Major Highway Link

Midtown Tunnel and Downtown Tunnel facilitate the movement of east-west traffic between Norfolk and Portsmouth. The removal of the Midtown Tunnel in the dynamic validation test diverted the traffic to the other highway facilities, primarily the Downtown Tunnel. Table 10.2 shows the average weekday traffic volumes on Midtown Tunnel and Downtown Tunnel in the static validation and the dynamic validation scenarios.

Table 10.2: Removal of Major Highway Link

All Day Volume Static Validation Midtown Tunnel 57,278 Downtown Tunnel 125,180 Total 182,458 Dynamic Validation Test: Removed major highway link - Midtown Tunnel Midtown Tunnel - Downtown Tunnel 145,386 Total 145,386 % Increase in Traffic on Downtown Tunnel due to removal of Midtown Tunnel 16.1%


Version 1.0 116

10.2.2 Addition of Major Highway Link

I-64 serves as the major highway facility for the north-south movements in Hampton, Williamsburg and Newport News. Jefferson Ave and Warwick Blvd, running parallel to I-64 also facilitate the north-south movements between Williamsburg and Newport News. For the purposes of dynamic validation, an additional lane was coded on I-64 in each direction between I-64/Jefferson Ave and I-64/Route 199. The additional lane provided additional capacity to the freeway route. The results from the scenario showed that the traffic from Jefferson Ave and Warwick Blvd running parallel to I-64 got diverted to I-64 due to its additional capacity. Table 10.3 shows the diversion of the average weekday traffic in northbound and southbound directions.

Table 10.3: Addition of Major Highway Link

All Day Vol,

NB All Day Vol,

SB Static Validation I-64 in Peninsula (Between Denbigh and Fort Eustis) 51,151 52,112 Jefferson Ave (Between Denbigh and Fort Eustis) 15,042 13,305 Warwick (Between Denbigh and Fort Eustis) 18,703 17,550 Total 84,896 82,967 Dynamic Validation Test: Added one additional lane to I-64 between Jefferson Ave and Route 199 I-64 in Peninsula (Between Denbigh and Fort Eustis), with 1 additional lane in each direction 60,257 60,917 Jefferson Ave (Between Denbigh and Fort Eustis) 8,259 7,808 Warwick (Between Denbigh and Fort Eustis) 16,624 15,342 Total 85,140 84,067 % Change in Traffic from Static Validation - I-64 17.8% 16.9% % Change in Traffic from Static Validation - Jefferson Ave -83.9% -85.0% % Change in Traffic from Static Validation - Warwick -67.5% -70.6%

10.2.3 Addition of Lane of Capacity to Major Highway Link

In this dynamic validation test, a lane was added to I-64 in each direction in Newport News and Hampton. The results from the scenario showed that the traffic from Jefferson Ave and Warwick Blvd running parallel to I-64 got diverted to I-64 due to the additional lane capacity. Table 10.4 shows the diversion of the average weekday traffic.


Version 1.0 117

Table 10.4: Addition of Lane of Capacity to Major Highway Link

All Day Vol,

NB All Day Vol,

SB Static Validation I-64 in Peninsula (Between J Clyde Morris and Harpersville) 72,515 76,309 Jefferson Ave (Between J Clyde Morris and Harpersville) 34,915 32,546 Warwick 13,866 12,392 Total 121,296 121,247 Dynamic Validation Test: Added one additional lane to I-64 in Peninsula in Newport News and Hampton (Between Yorktown and Norfolk) in both directions I-64 in Peninsula (Between J Clyde Morris and Harpersville), with 1 additional lane 76,294 79,435 Jefferson Ave (Between J Clyde Morris and Harpersville) 33,042 31,265 Warwick 12,839 11,549 Total 122,175 122,249 % Change in Traffic from Static Validation - I-64 5.2% 4.1% % Change in Traffic from Static Validation - Jefferson Ave -54.4% -59.0% % Change in Traffic from Static Validation - Warwick -82.3% -84.9%

10.2.4 Change in Free Flow Speed

In this dynamic validation test, the free flow speed on I-64 was increased by 10 mph in each direction in Newport News and Hampton. The results from the scenario showed that the traffic from Jefferson Ave and Warwick Blvd running parallel to I-64 got diverted to I-64 due to faster speeds. Table 10.5 shows the diversion of the average weekday traffic.

Table 10.5: Change in Free Flow Speed

All Day Vol,

NB All Day Vol,

SB Static Validation I-64 in Peninsula (Between J Clyde Morris and Harpersville) 72,515 76,309 Jefferson Ave (Between J Clyde Morris and Harpersville) 34,915 32,546 Warwick 13,866 12,392 Total 121,296 121,247 Dynamic Validation Test: Added 10 mph to free flow speed on I-64 in Newport News and Hampton (Between Yorktown and Norfolk) in both directions I-64 in Peninsula (Between J Clyde Morris and Harpersville) 81,497 84,572 Jefferson Ave (Between J Clyde Morris and Harpersville) 30,637 29,451 Warwick 11,549 10,675 Total 123,683 124,698 % Change from Static Validation - I-64 12.4% 10.8% % Change from Static Validation - Jefferson Ave -57.8% -61.4% % Change from Static Validation - Warwick -84.1% -86.0%


Version 1.0 118

10.3 Sensitivity Tests with Changes in Travel Cost

The sensitivity of the model to changes in travel costs were tested using the inputs from HRTPO and the dynamic validation evaluation criteria measures from the VTM. The following scenarios were tested:

• Addition of $5 toll on major highway links • Increase in Value of Time (VOT) by 50% • Increase in Gas Cost to $5/gallon

10.3.1 Addition of $5 Toll on Major Highway Links

The Hampton Roads model base year 2009 scenario has toll facilities coded on George P. Coleman Bridge (Highway 17) and Chesapeake Expressway. The dynamic validation scenario includes an addition of $5 fixed toll on Midtown Tunnel, Downtown Tunnel and High Rise Bridge in addition to the existing toll facilities. The tolls put a high travel cost on the major east-west highway facilities between Norfolk/Chesapeake and Portsmouth. As a result, the travelers chose to take alternate routes to go their destinations in the dynamic validation scenario. The alternate routes included Military Highway, I-64 between Norfolk and Hampton, I-664 between Portsmouth and Newport News and Moses Grandy Trail (Route 165). Table 10.6 shows the diversion of the average weekday traffic from Midtown Tunnel, Downtown Tunnel and High Ridge Bridge to other highway facilities.

Table 10.6: Addition of $5 Toll on Midtown Tunnel, Downtown Tunnel and High Rise Bridge

All Day Volume Vehicle Trips Static Validation 5,413,926 Midtown Tunnel 57,278 Downtown Tunnel 125,180 High Rise Bridge 71,073 Subtotal on Midtown, Downtown Tunnels and High Rise Bridge 253,531 Military Hwy (Alternate Route) 49,599 I-64 Connecting Hampton and Norfolk (Alternate Route) 90,350 Moses Grandy Trail (Alternate Route) 31,474 Subtotal on Alternate Routes 171,423 Dynamic Validation Test: Added toll of $5.00 5,412,300 Midtown Tunnel - $5 Toll 16,559 Downtown Tunnel - $5 Toll 38,536 High Rise Bridge - $5 Toll 9,815 Subtotal 64,910 Military Hwy (Alternate Route) 112,084 I-64 Connecting Hampton and Norfolk (Alternate Route) 121,993 Moses Grandy Trail (Alternate Route) 48,070 Subtotal on Alternate Routes 282,147 % Change from Static Validation for Midtown, Downtown Tunnels and High Rise Bridge -74.4%


Version 1.0 119

10.3.2 Increase in Value of Time (VOT) by 50%

In this dynamic validation test, the original VOT of $10/hour (16.67 cents/minute) was increased to $15/hour (25 cents/minute). The cost coefficient in the mode choice model was also updated to reflect an increase in VOT by 50%. The results as shown in Table 10.7 indicated that an increase in VOT increased vehicle trips, increased the transit mode shares and decreased the auto mode shares.

Table 10.7: Increase in VOT by 50%

Vehicle Trips

Auto Mode Share

Transit Mode Share

Static Validation 5,413,926 99.36% 0.64% Dynamic Validation Test: Increase VOT by 50%, ie. $10/hour to $15/hour 5,448,566 99.33% 0.67%

% Change from Static Validation 0.6% -0.03% 5.3%

10.3.3 Increase in Gas Cost to $5/Gallon

This dynamic validation test involved increasing the gas cost to $5/gallon to test the sensitivity of the model to choose between automobile modes and transit modes. The base year model uses 10.5 cents/mile as the auto operating cost. Assuming an average mileage of automobiles as 20 miles/gallon, the effective gas cost is about $2.10/gallon in the base year scenario. For the purposes of the dynamic validation, the gas cost was increased to $5/gallon or 25 cents/mile. The results shown in Table 10.8 indicated that the travelers chose transit when the gas prices increased. As a result, there was a decrease in the vehicle trips, VMT and auto mode shares and an increase in the transit mode share.

Table 10.8: Increase in Gas Cost to $5/Gallon

Vehicle Trips VMT Auto Mode

Share Transit

Mode Share

Static Validation 5,413,926 36,149,968 99.36% 0.64% Dynamic Validation: Increase gas cost to $5/gal, i.e. Auto Op Cost of 25 cents/mile 5,272,653 34,233,146 99.34% 0.66%

% Change from Static Validation -2.6% -5.3% -0.02% 3.2%


Version 1.0 120

10.4 Sensitivity Tests with Changes in Transit Service

The sensitivity of the model to transit fares, headways and transit routes was tested using the following dynamic validation scenarios:

• Free fares on transit • Increase in fares by 100% • Increase in transit headways by 50% • Decrease in transit headways by 50% • Addition of a new transit line to a major corridor

The evaluation criteria for these tests included change in transit ridership and change in transit mode shares between the dynamic validation tests and the static validation scenario.

10.4.1 Free Fares on Transit

This dynamic validation test was performed to evaluate the model’s sensitivity to changes in transit fares. In this test, the transit was coded to have no (or zero) fares keeping all other model inputs the same as the static validation base year scenario. The results showed an increase in transit ridership and transit mode shares and a decrease in auto mode shares and vehicle trips. The results are summarized in Table 10.9.

The VTM states Simpson-Curtin rule of thumb which holds that the transit fare elasticity is about -0.30. That means that a 10% increase in transit fare should show about 3% decrease in transit ridership. As per the rule, free transit should increase the transit ridership by about 30%. The Hampton Roads model shows an increase in the transit ridership by 26% with free transit.

Table 10.9: Free Transit Fares

Vehicle rips Auto Mode

Share Transit

Mode Share Transit

Ridership

Static Validation 5,413,926 99.36% 0.64% 55,365 Dynamic Validation Test: Free Fares on Transit 5,406,251 99.22% 0.78% 69,638 % Change from Static Validation -0.1% -0.1% 22.9% 25.8%

10.4.2 Increase in Transit Fares by 100%

This dynamic test involved increasing the transit fares by 100%. The transit fares including the transfer fares coded on all transit modes were doubled in the model. The results showed a shift from transit modes to auto modes as shown in Table 10.10. There was a decrease transit ridership, and an increase in the vehicle trips.


Version 1.0 121

Table 10.10: Doubled Transit Fares

Vehicle Trips

Auto Mode Share

Transit Mode Share

Transit Ridership

Static Validation 5,413,926 99.36% 0.64% 55,365 Dynamic Validation Test: 100% Increase on Fares on Transit 5,419,882 99.48% 0.52% 44,777 % Change from Static Validation 0.1% 0.1% -17.9% -19.1%

10.4.3 Increase in Transit Headways by 50%

In this test, the headways on all transit lines were increased by 50% thus reducing the frequency of the routes. The reduction in transit frequency decreased the transit ridership and transit mode shares, and increased the auto mode shares, VMT and vehicle trips. Table 10.11 shows the comparison of the mode shares, vehicle trips, VMT and transit ridership between the static validation and dynamic validation scenarios.

Table 10.11: Increase in Transit Headways by 50%


Share Transit

Mode Share Transit

Ridership

Static Validation 5,413,926 36,149,968 99.36% 0.64% 55,365 Dynamic Validation Test: Increasing transit headways by 50% 5,416,732 36,179,591 99.52% 0.48% 38,650

% Change from Static Validation 0.1% 0.1% 0.2% -24.0% -30.2%

10.4.4 Decrease in Transit Headways by 50%

In this test, the headways on all transit lines were decreased by 50% thus increasing the frequency of the transit routes. The increase in transit frequency increased the transit ridership and transit mode shares, and decreased the auto mode shares, VMT and vehicle trips. Table 10.12 shows the comparison of the evaluation statistics between static validation and dynamic validation runs.

Table 10.12: Decrease in Transit Headways by 50%


Share Transit

Mode Share Transit

Ridership

Static Validation 5,413,926 36,149,968 99.36% 0.64% 55,365 Dynamic Validation Test: Decreasing transit headways by 50% 5,401,244 36,091,940 99.12% 0.88% 84,703

% Change from Static Validation -0.2% -0.2% -0.2% 38.9% 53.0%


Version 1.0 122

10.4.5 Addition of New Transit Line

A hypothetical new bus route similar to existing HRT Route 20 from Virginia Beach to Norfolk Downtown was coded in this dynamic validation test. The new bus route was coded at 15 minute headway while retaining the existing Route 20 at 30 minute headway. The results showed an increase in overall transit ridership due to the addition of the new bus route. Since the new Route 20 had a better frequency than the existing Route 20, the transit ridership from the existing Route 20 got diverted to the new Route 20. Table 10.13 shows the total transit ridership, the ridership on the existing Route 20 in the base year scenario (static validation) and the ridership on the existing as well as the new Route 20 in the dynamic validation scenario.

Table 10.13: Addition of New Transit Route

Total Transit

Ridership

Transit Ridership on

Route 20

Static Validation 55,365 4,738

Dynamic Validation Test: Added a transit route similar to Route 20 Bus at 15 min headway, in addition to the existing Route 20 at 30 min headway 57,797 Existing Route 20 at 30 min headway 2,775 New Route 20 at 15 min headway 4,401 Total Route 20 Boardings 7,176

% Change on Route 20 from Static Validation 4.4% 51.5%

10.5 Future Year Test Forecast

VDOT in coordination with HRTPO have prepared and tested the future year networks for years 2018 and 2034.


Version 1.0 123

LIST OF REFERENCES

The 2009 National Household Travel Survey (NHTS) Virginia Add-On conducted for VDOT by the Federal Highway Administration (FHWA) for calibration of Trip Generation and Trip Distribution models

2000 Census Transportation Planning Package (CTPP) Journey-To-Work survey for Trip Distribution validation.

National Transit Database (NTD), 2009 (www.ntdprogram.gov) for Transit validation.

Comprehensive Operations Analysis Surveys supplemented by additional surveys for Virginia Beach Fixed Guideway project, Hampton Roads Transit, 2009 for Transit validation.

Metropolitan Washington Council of Governments (MWCOG) model.

Virginia Beach Transit Extension Study (VBTES) Calibration and Validation of the Ridership Model Report, December 2012, Hampton Roads Transit (HRT), for Transit model update.

HRTPO planning districts for analysis of NHTS data.

Hampton Roads Travel Forecasting Model, Truck Model Technical Report, June 30, 2010, for Truck E-E shares.

South Florida Stated Preference Travel Survey and Toll Mode Choice Model, July 2006, for Value of Time (VOT) assumptions.

Norfolk LRT Project Final Design Patronage Forecasting Report, 2007, for Mode Choice model development.

Traffic Monitoring Guide - May 1, 2001; Section 4: Vehicle Classification Monitoring, http://www.fhwa.dot.gov/ohim/tmguide/tmg4.htm#tab4a1

NCHRP Synthesis 298, Truck Trip Generation Data, 2001, for Truck Trip Generation model development.

Free-flow speeds and congested speeds from 2010 INRIX data (www.inrix.com)

Daily and hourly counts from VDOT’s official Traffic Monitoring System (TMS)

Evaluation of Volume-Delay Functions (VDF) and Their Implementation in VDOT Travel Demand Models, May 2011, Virginia Modeling, Analysis and Simulation Center (VMASC) at Old Dominion University.

http://www.ntdprogram.gov/

http://www.fhwa.dot.gov/ohim/tmguide/tmg4.htm#tab4a1


HAMPTON ROADS MODEL METHODOLOGY REPORT (Ver. 1.0) · VDOT Project ID:30681-01-02 Hampton Roads...

Documents

Transcript of HAMPTON ROADS MODEL METHODOLOGY REPORT (Ver. 1.0) · VDOT Project ID:30681-01-02 Hampton Roads...