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Publication Number:  FHWA-HRT-13-022    Date:  August 2013
Publication Number: FHWA-HRT-13-022
Date: August 2013


Synthesis of Traveler Choice Research: Improving Modeling Accuracy for Better Transportation Decisionmaking

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Travelers’ choices are central to the performance of a transportation system, but little is known about what influences such choices or the impact they have on system performance. When selecting a transportation management strategy, a transportation management center operator must understand and anticipate how travelers will respond: Will they stay on the same routes or divert? Will they decide to walk, bike, or take a bus or train instead of driving? Will they leave earlier or later?


The operator must know the potential benefits of alternative overall strategies (e.g., variable pricing or information on dynamic message signs) as well as how to handle day-to-day operations by implementing strategies to provide effective responses to particular events. The operator must also account for non-network, predisposing factors that influence travelers’ choices. Such factors, including land use, population density, and walkability, are generally out of the control of the network manager, and their influence may not be intuitively obvious.


The project “Analysis of Traffic Network and Non-Network Impacts Upon Traveler Choice” addresses the current state of the practice, advances understanding, and identifies gaps in knowledge regarding traveler choices. This synthesis report documents the project’s first major activity: an assessment of current research and practices in traveler choice. It will be a resource for both traveler choice researchers and organizations considering transportation management strategies that influence traveler choice. This report also lays the foundation for the project’s next step, the development of traveler choice models that can be incorporated into existing transportation analysis tools.


Joseph I. Peters
Director, Office of Operations
Research and Development



This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. This report does not constitute a standard, specification, or regulation.


The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document.


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Technical Report Documentation Page

1. Report No.


2. Government Accession No.


3. Recipient’s Catalog No.


4. Title and Subtitle

Synthesis of Traveler Choice Research: Improving Modeling Accuracy for Better Transportation Decisionmaking

5. Report Date

August 2013

6. Performing Organization Code


7. Author(s)

Hani S. Mahmassani, Frank Koppelman, Charlotte Frei, Andreas Frei, and Robert Haas

8. Performing Organization Report No.


9. Performing Organization Name and Address


1710 SAIC Drive

McLean, VA 22102

Northwestern University

The Transportation Center

600 Foster Street

Evanston, IL 60208-4055

10. Work Unit No. (TRAIS)


11. Contract or Grant No.


12. Sponsoring Agency Name and Address

Office of Operations

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered


14. Sponsoring Agency Code


15. Supplementary Notes

The Contracting Officer’s Technical Representatives (COTRs) were Taylor W.P. Lochrane and David C.Y. Yang.

16. Abstract

Over the last 50 years, advances in the fields of travel behavior research and travel demand forecasting have been immense, driven by the increasing costs of infrastructure and spatial limitations in areas of high population density together with externalities in these areas. The field has changed from supply-oriented planning to incorporating and managing demand. As such, methods from a variety of disciplines have been borrowed and extended to explain human behavior and interaction. Many experts have called for better data collection and methods of analysis across a number of time horizons, that is, integrated supply and demand models that capture travel behavior over time and space. A new paradigm may be called for to address the present challenges of model integration; user preferences, heterogeneity, and endogeneity; habitual behavior; and human socializing. This report provides a synthesis of the state of knowledge in travel behavior research and identifies gaps in existing data, methods, and practices that must be filled to meet the analysis needs of an emerging class of supply- and demand-side interventions that seek to leverage the opportunities of real-time information.

17. Key Words

Travel behavior, Active Transportation Demand Management (ATDM), Managing Travel Demand (MTD), Integrated Corridor Management (ICM), Active Traffic Management (ATM), User behavior response

18. Distribution Statement

No restrictions. This document is available to the public through NTIS:

National Technical Information Service

Springfield, VA 22161

19. Security Classif. (of this report)


20. Security Classif. (of this page)


21. No. of Pages


22. Price


 Form DOT F 1700.7 (8-72)                                                                                        Reproduction of completed page authorized


SI* (Modern Metric) Conversion Factors

Table of Contents

Objectives, Motivation, and Structure.. 1

Scope and Conceptual Framework.. 3

Background.. 3

Scope Delineation.. 3

Conceptual framework.. 4

Travel Behavior Studies Review... 7

Day-to-Day and Within-Day Behavior Changes. 7

Effect of Tolling and Other Costs on Mobility Decisions. 8

Walk Quality on Day-to-Day Travel Behavior and Patterns. 9

Behavioral Pattern Changes. 10

Sociodemographics and Household Composition. 10

Effect of Travel Demand Management Measures and Parking Pricing on Mode Choice  11

Learning, Experience, and Inertia. 11

Lifestyle- and Mobility-Based Behavior Changes. 12

Effect of Transit-Oriented Development/Density on Behavior Patterns and Long-Term Choices. 12

Residential Self-Selection and Vehicle Ownership. 13

Behavioral Mechanisms. 14

Household Interactions. 15

Social Networks. 17

Travel Behavior Models Review... 19

Activity-Based Models. 19

Utility-Based Econometric Models. 19

Rule-Based Computational Process Models. 21

Agent-Based Modeling Systems. 22

Model Integration.. 22

Travel Behavior Data Review... 25

Standard Sources. 25

Travel Diaries. 25

Stated Preference and Revealed Preference.. 26

Emerging Potential in Data Collection.. 27

Growing Role of Experiments and Gaming Methods. 28

Conclusions, Recommendations, and Future Research.. 31

References. 33

Bibliography.. 45


List of figures

Figure 1. Chart. Conceptual Framework.

Figure 2. Equation. Generalized Indifference Band Framework.

Figure 3. Equation. Random Utility Formulation.

Figure 4. Equation. Joint Versus Single Utility Threshold.

Figure 5. Equation. Weighted Sum of Joint Versus Single Utility Threshold.


List of tables

Table 1. Recent and Continuous National Travel Surveys

List of Acronyms

ATDM             Active Transportation and Demand Management

ATIS                Advanced traveler information systems

GPS                 Global Positioning System

HOV                High-occupancy vehicle

ICM                 Integrated Corridor Management

IT                     Information technology

MATSim          Multi-Agent Transport Simulation Toolkit

MTD                Managing Travel Demand

RP                   Revealed preference

SOV                Single-occupancy vehicle

SP                    Stated preference

TCRP              Transit Cooperative Research Program

TRANSIMS    Transportation Analysis and Simulation System

VMT                Vehicle miles traveled


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