U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
| REPORT |
| This report is an archived publication and may contain dated technical, contact, and link information |
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| Publication Number: FHWA-HRT-13-022 Date: August 2013 |
Publication Number: FHWA-HRT-13-022 Date: August 2013 |
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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
Notice
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
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1. Report No. FHWA-HRT-13-022 |
2. Government Accession No.
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3. Recipient’s Catalog No.
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4. Title and Subtitle Synthesis of Traveler Choice Research: Improving Modeling Accuracy for Better Transportation Decisionmaking |
5. Report Date August 2013 |
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6. Performing Organization Code
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7. Author(s) Hani S. Mahmassani, Frank Koppelman, Charlotte Frei, Andreas Frei, and Robert Haas |
8. Performing Organization Report No.
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9. Performing Organization Name and Address SAIC 1710 SAIC Drive McLean, VA 22102 Northwestern University The Transportation Center 600 Foster Street Evanston, IL 60208-4055 |
10. Work Unit No. (TRAIS)
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11. Contract or Grant No. DTFH61-06-D-00005 |
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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
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14. Sponsoring Agency Code
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15. Supplementary Notes The Contracting Officer’s Technical Representatives (COTRs) were Taylor W.P. Lochrane and David C.Y. Yang. |
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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. |
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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 |
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19. Security Classif. (of this report) Unclassified |
20. Security Classif. (of this page) Unclassified |
21. No. of Pages 55 |
22. Price |
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Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
SI* (Modern Metric) Conversion Factors
Objectives, Motivation, and Structure
Scope and Conceptual Framework
Travel Behavior Studies Review
Day-to-Day and Within-Day Behavior Changes
Effect of Tolling and Other Costs on Mobility Decisions
Walk Quality on Day-to-Day Travel Behavior and Patterns
Sociodemographics and Household Composition
Effect of Travel Demand Management Measures and Parking Pricing on Mode Choice
Learning, Experience, and Inertia
Lifestyle- and Mobility-Based Behavior Changes
Effect of Transit-Oriented Development/Density on Behavior Patterns and Long-Term Choices
Residential Self-Selection and Vehicle Ownership
Utility-Based Econometric Models
Rule-Based Computational Process Models
Stated Preference and Revealed Preference
Emerging Potential in Data Collection
Growing Role of Experiments and Gaming Methods
Conclusions, Recommendations, and Future Research
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.
Table 1. Recent and Continuous National Travel Surveys
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
