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IMPLEMENTATION OF ROAD PRICING MODEL WITH THE EMME/2 MACRO LANGUAGE

Jamie Wheway (Director) Karl Cheuk (Transport Analyst)

Wilbur Smith Associates Limited

(Hong Kong)

Tel: (852) 2359-5700 Fax: (852) 2385-7215

E-mail: hkchina@wilbursmith.com

1st Asian EMME/2 Users Conference Shanghai

August 23-24, 1999

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ABSTRACT

Traffic congestion, in Hong Kong, is a serious problem and is unlikely to be solved by adding more road capacity. Due to spatial limitation in the most congested areas and latent demand induced by the added capacity, transport planners and economists have shifted their focus from the supply side to the demand side for tackling traffic congestion. Road pricing can form part of a traffic demand management strategy to provide an efficient, equitable and flexible solution to handle traffic congestion problems in the long term. Hong Kong Government appointed a consultancy to investigate the requirement for electronic road pricing (ERP) system implementation, the social impacts and its implications. As a part of the study requirement, A discrete choice model with multinomial logit formulation was developed in order to assess major behavioural change in traveller responses due to ERP. From a review of the previous Hong Kong ERP study and other worldwide experience, the most likely responses of travellers to ERP are identified as paying the road pricing charge, changing the time of travel, changing the route taken, switching to other modes, switching to use Park and Ride facility, increasing vehicle occupancy for ERP cost sharing and suppressing of trip making. A stated preference (SP) survey was designed to collect information upon individuals likely responses to the alternative ERP responses defined above. The SP survey results were analysed to derive the ERP assessment model coefficients. The model was integrated with an existing multi-modal, four-step model to produce a tool capable of analysing alternative ERP strategies by using EMME/2. INTRODUCTION

Over the past years, the number of vehicles on the road has increased at a rate much faster than the rate of transport infrastructure to be built and conventional traffic management schemes, such as first registration taxation and fuel taxation, are able to accommodate. Hong Kong Government considers that road pricing, it might provide a more efficient, equitable and flexible solution to deal with traffic congestion in long term. It was concluded from a review of worldwide studies of road pricing and the 1985 Hong Kong Pilot Scheme that the introduction of electronic road pricing schemes (ERP) would cause major behavioural changes on travel decisions. From the transport modelling point of view, the existing strategic transport model (ECTS-2 Model) that was originally developed to handle various traditional transport policy tests is unable to model the possible behavioural changes of travellers due to the road pricing. A discrete choice sub-model was therefore introduced for this purpose and named ERP Assessment Model. Only the assessment model for car and taxi trips will be discussed in following sections.

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This paper is divided into four parts that includes overviews of the ECTS-2 model, structure of the assessment model, implementation of the assessment model and conclusion.

OVERVIEW OF ECTS-2 MODEL

The ECTS-2 Model is a part of a series of comprehensive transport models that have been developed by Hong Kong Government for more than 20 years to plan transport infrastructures and to assess various transport policies. The ECTS-2 Model was developed during the Model Enhancement Study 1993 - 1995 and is a four-step model comprising trip generation and attraction, trip distribution, modal split and trip assignment. The structure of the model is illustrated in Figure 1. The ECTS-2 Model was originally developed using WSAs transport planning package WAYS. The model was converted to EMME/2 on a module-by-module basis to ensure that the conversion was able to reproduce ECTS-2 results at each stage of the model process. The model was calibrated using data collected in the 1992 Travel Characteristics Survey Study (TCS) and validated against observed traffic count and public transport usage data. Further model updating and refinement were performed in order to replicate 1996 traffic conditions. The updating of model involved assembling and inputting 1996 land use planning data, transport networks and transport costs. The model refinement, on the other hand, included disaggregating the home-based work trip into three different income bands instead of two used in the ECTS-2 Model, enchancing the car availability model to handle zonal monthly usage costs, enhancing the peak model and introducing more traffic analysis zones for auto assignment. STRUCTURE OF ERP ASSESSMENT MODEL The assessment model is constructed by considering the four key issues: the behavioural segments for which equation coefficients are developed; the behavioural choices for which equation coefficients are developed; the independent variables to be used in the utility functions; and the mathematical formulation of the model. Behavioural Segmentation Income is the one of the most important socioeconomic factors and it is incorporated into various model segments in different ways. For home based work trips the model is segmented by three income levels: low income (below HK$25,000 per month); medium income (between HK$25,000 and HK$40,000 per month); and

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high income (above HK$40,000 per month).

Tests on other socioeconomic factors were undertaken on such variables as household size, vehicle ownership per household and age. No significant or only marginal improvements in statistical sense on the model fit were found. In order to maintain the model simplicity and maximize the computational efficiency, none of these variables were selected in the model. Home to work and work to home trips are found to have similar sensitivities to travel time, cost, and trip start time, and the two trips are consequently combined into a single work segment. Because of the similarities of the home to school trips with the above work-related trips, the home to school trips were merged into the work segment for estimating the model coefficients. Only one coefficient that differed between work and school trips, travel time, was specifically estimated for the two trips. Similarly, the home based other segment was created by merging the home to other and other to home trips. Employers business trips and non-home based trips each differs in time and cost sensitivity from each of the previously described model segments. These two trip purposes comprise two additional model segments. According to the above analysis, the final model segmentations are categorized as follows: Home based work low income; Home based work medium income; Home based work high income; Home based school; Home based other; Non home based ; and Employers business. Behavioural Choices According to our worldwide experiences in congestion pricing studies and reviewing the previous road pricing study in Hong Kong, several possible travel behavioural choices were identified as follows: paying the road pricing charge; changing the time of travel; changing the route taken; switching to other modes; switching to use Park and Ride facility; increasing vehicle occupancy for ERP cost sharing; and suppressing of trip making. For car trips, six of the above choices are modelled in the assessment model. The route choice option is installed in the auto assignment model. For the taxi trips, only

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three options, paying the road pricing, changing the time of travel and switching to alternative public transport modes, are implemented in the assessment model. This was concluded from taking the existing travel pattern and behaviour into account. Similar to car trips, the route choice option for taxi trip is also installed in the assignment model. Independent Variables Characteristics of trips being made and of the modes that are available for the trip are described in utility functions for the choice alternatives. The utility functions are modelled as affecting choice probabilities through the multinomial logit model in functional form. Separate utility functions are specified in the model for each possible alternative choice. Coefficients of the variables in all utility functions were estimated by using maximum likelihood estimators from the data collected from a stated preference (SP) survey. The specifications of utility functions were developed in a series of tests involving each of the classes of factors that affect mode choices. A total of 140 tests were performed and the final utility formulation is a linear combination of variables included travel time for auto and transit modes, ERP charge, out-of-pocket expenditures including fuel costs and road tolls, and transport fares. Constant terms of the linear utility functions were individually investigated in order to improve the model fit. The final model adopts constant terms for the alternative choices of occupancy shifting, public transport mode, Park and Ride facility and trip suppression. Mathematical formulation of the model The mathematical formulation of the model is determined by whether the model structure is nested or not, a nested structure implying a certain degree of sequential choices in the process of decision making. Several nesting structures were statistically analysed and the estimated nest coefficient thetas were not statistically significant different from zero. It is concluded that the simplest multinomial logit structure was appropriate for the behavourial choices among the possible alternatives. The probability of each prescribed alternative choice are calculated by using the multinomial logit formulation that is one of the most popular tools used in discrete choice modelling. The model formulation among the choice probabilities is recorded below:

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kModesall

U

U

ij kij

Zij

e

eZp

where

Pij(Z) is the probability that alternative Z will be chosen for origin-destination movement from zone i to zone j,

is an adjustment factor and k

ij

Z

ijUU , are utility functions for alternatives Z and k.

The utility functions in the above formulation are individually described below:

k

ij

k

ij

ijERPij

ERPVoT

FeeERP

VoT

CTU

TkERPVoT

FeeERP

VoT

CTU

k

ij

k

ij

ijtime

ij

1

*

26.0 kERPVoT

FeeERP

VoT

CTU

k

ij

k

ij

ijoccij

3kUnotripij

4kVoT

PTCPTT

VoT

CTU

k

ij

ij

k

ij

ijPnRij

5kVoT

PTCPTTU

k

ij

ijPTij

where

ERPijU is the utility for ERP choice;

timeijU is the utility for time shift choice;

occijU is the utility for occupancy shift choice;

notripijU is the utility for no trip choice;

PnRijU is the utility for Park and Ride choice;

PTijU is the utility for Public Transport choice;

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ijT is the travel time in minutes skimmed from highway network;

ijPTT is the travel time in minutes skimmed from transit network;

ijC is the travel cost in dollars skimmed from highway network;

ijPTC is the travel cost in dollars skimmed from transit network;

kVoT is the value of time for out-of-pocket costs by purpose k;

kERPVoT is the value of time for ERP by purpose k;

ijFeeERP is the ERP charge in dollars between zone i and j; *ijFeeERP is the ERP charge in time period moved into between zone i

and j;

T is the number of minutes time switching by;

1K is the change in time choice constant, purpose K;

2K is the occupancy shift choice constant, purpose K;

3K is the no trip choice constant, purpose K;

4K is the Park and Ride choice constant, purpose K; and

5K is the transit mode choice constant, purpose K.

IMPLEMENTATION OF ERP ASSESSMENT MODEL The ERP assessment model has been coded in EMME/2 macro language with three macros that correspond to three model stages: calculation of slice matrices by trip purpose and by mode (slice macro); calculation of utility matrices by trip purpose and by mode (utility macro); and calculation of trip demands for all alternative behavioural choices (logit macro). The order of these macros within the model and the interaction between the assessment model and the ECTS-2 model is illustrated in Figure 2. These interactions are summarised as follows: Daily car and taxi trip matrices by trip purposes are extracted from the databank

for the ECTS-2 Model and temporarily stored in the databank for the slice macro. This macro outputs 98 car and taxi time slice matrices: 14 slices by 7 trip purposes;

Auto and public transport time and cost skim matrices are extracted from the databank for the ECTS-2 Model and temporarily stored in the databank for utility macro. The macro also considers the parameters derived from the SP survey and computes the utilities for each behavioural choice by time slices by trip purposes for car and taxi modes. There are totally 588 skim matrices for car trips (6 choices by 14 slices by 7 trip purposes) and 298 skim matrices for taxi trips (3 choices by 14 slices by 7 trip purpose) generated.

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The matrices created from the above two stages are then imported into the databank for the macro logit. It calculates probabilities of each possible choice and derives choice matrices that are totally 588 car matrices and 298 taxi matrices as described above.

The final step is to produce car and taxi trip tables by 6 periods by trip purpose which are sent to the databank for assignment process.

The macro running sequence of the ERP assessment model is illustrated as Figure 3. The macros for slice, utility and logit are sequentially executed in the order for a particular time slice and for a journey purpose prior to any computation of next time slice and purpose. Starting with journey purpose 1, the model first creates the 14 time slice trip tables and then calculates the utilities and choice probabilities for the first time slice trip matrices. The model then iterates around loop A for the other 14 time slices. Once this has been completed, the model iterates around loop B for the journey purpose 2 and the process continues up to the 14th time slice for the 7th trip purpose matrices. Slice Model In the model daily car and taxi matrices by trip purpose from the ECTS-2 model are disaggregated into 14 slices by trip purpose. The daily production-attraction trip tables for each purpose is split into AM and PM peak period origin-destination matrices in similar fashion to the ECTS-2 Model. Also, subtracting the total peak demands from the daily matrices derives the off-peak matrices. The period definition for car and taxi trips is AM Peak from 07:45 to 09:30; Off peak from 09:30 to 16:45; and PM Peak from 16:45 to 18:30. The period matrices are then sliced into 14 components by applying the time slice factors by trip purposes that are derived from the data extracted from the 1992 Travel Characteristic Survey Study. Utility Model The calculation of linear utilities for all choices by modes and by trip purposes is performed in this macro. The mathematical formulation is recorded in the section of the mathematical formulation of assessment model. Logit Model The logit model calculates the probability of each behavioural choice and its demands by time slice, by trip purpose and by mode. The resultant matrices are then processed in the macro with steps:

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The park and ride choice slice matrices are split into trip leg matrices for car and

public components and combined to produce time period matrices. the ERP choice slice matrices are aggregated into six period matrices while the

trip demands with choice of time shifting and the auto legs from Park and Ride choice demands are added with the ERP choice period matrices in appropriate time periods. The definition of time periods is named as AM shoulder, AM peak, Off-peak, PM peak, PM shoulder and Night.

The occupancy and trip suppression choice slice demands are only reported to ASCII files that are prepared for input to spreadsheets for further analysis.

The public transport choice slice demands with transit legs from Park and Ride choice demands are aggregated into period matrices and delivered to the ECTS-2 model.

CONCLUSION The road pricing assessment model is a short-term strategic transport model improvement developed in the course of the Electronic Road Pricing Feasibility Study. The model is an add-on component to the ECTS-2 model and overcomes the existing model restriction such as its ability to handle Park and Ride trips, time shifting effect due to congestion or road pricing, etc. The assessment model and the ECTS-2 model have been successfully implemented under the EMME/2 platform and macro language. The model has been examined by undertaking several hundred sensitivity tests on various transport policy assumptions that includes various road pricing structures.

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Figure 1

ECTS-2 Model Structure

Goods Vehicle Model

Public Transport Data

Highway Data

Auto Network Model

PT Network Model

Planning Data

Household Income Model

Trip Attraction Model

Trip Generation Model

Car Availability Model

Cost Model

Trip Distribution/ Modal Split

Model

Car/Taxi Model

PT Sub-Modal Split Model

PT Assignment

Model

Auto Assignment

Model

PT Peak Model ERP Assessment

Model

Cost Data

Special Travel Model

Analysis and Evaluation

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Figure 2

Relationship between ECTS-2 Model and ERP Assessment Model

ECTS-2 Model

ERP Assessment Model

Slice Macro

Demand Matrices by Purposes and

Time Slices

Utility Macro

Utilities for Each Choice, by

Purposes and by Time Slices

Logit Macro

Trip Tables for Each Choice, by Purpose and by

Time Slices

Daily Trip Matrices by

Purposes

Cost Skims by Modes and Purposes

Assignment Routines

SP Model Parameters

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SLICE MACRO

UTILITY MACRO

LOGIT

MACRO

LOOP A Slices 1 to 14

LOOP B Purpose 1 to 7

Figure 3

ERP Assessment Model Macro Running Sequence