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First published online January 1, 2011

Predicting Route Choices of Drivers Given Categorical and Numerical Information on Delays Ahead: Effects of Age, Experience, and Prior Knowledge

Abstract

In recent years there has been a considerable increase in the systems used to provide real-time traffic information to motorists. Examples of such systems include dynamic message signs and 511 travel information systems. However, such systems can be used to reduce congestion—one of their primary purposes—only if one can predict the route choices of drivers as a function of the information displayed. This simulator study looks at the diversion pattern that occurs when delays are reported ahead on the main route and how these diversion patterns vary as a function of delay times (for numerical delay signs), message content (for categorical delay signs), use of 511, and drivers’ familiarity with the alternative route travel times across two different age groups. For numerical delay signs, the study shows that one can reliably predict the diversion frequencies at the different delays and across the different ages; then it is possible for traffic engineers to know ahead of time how likely it is for drivers to take an alternative route. For categorical delay signs, the findings indicate that drivers’ knowledge of the alternative route travel time affects the choices of older versus younger or middle-aged adults differently. When the times are not known, the two groups behave differently; when the times are known, the groups behave similarly. This finding suggests that traffic engineers should try where possible to present the alternative route travel times as well as the delays on the main route.

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References

1. Kwon J., Mauch M., and Varaiya P. Components of Congestion: Delay from Incidents, Special Events, Lane Closures, Weather, Potential Ramp Metering Gain, and Excess Demand. In Transportation Research Record: Journal of the Transportation Research Board, No. 1959, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 84–91.
2. U.S. Traffic Delays Skyrocket. Public Works Magazine, July 1, 2005. http://www.pwmag.com/industrynews.asp?sectionID=%20770&articleID=271500. Accessed July 25, 2010.
3. Bonsall P., and Parry T. Using an Interactive Route-Choice Simulator to Investigate Drivers’ Compliance with Route Guidance Advice. In Transportation Research Record 1306, TRB, National Research Council, Washington, D.C., 1991, pp. 59–68.
4. Ben-Elia E. E. The Combined Effect of Information and Experience on Drivers’ Route-Choice Behavior. Transportation, Vol. 35, No. 2, 2008, pp. 165–177.
5. Abdel-Aty M. A. Modeling Drivers’ Diversion from Normal Routes Under ATIS Using Generalized Estimating Equations and Binomial Pro-bit Link Function. Transportation, Vol. 31, No. 3, 2004, pp. 327–348.
6. Katsikopoulos K. V., and Fisher D. L. Risk Attitude Reversals in Drivers’ Route Choice When Range of Travel Time Information Is Provided. Human Factors, Vol. 44, No. 3, 2002, pp. 466–473.
7. Katsikopoulos K. V., and Fisher D. L. The Framing of Drivers’ Route Choices When Travel Time Information Is Provided Under Varying Degrees of Cognitive Load. Human Factors, Vol. 42, No. 3, 2000, pp. 470–481.
8. Simon H. A. Models of Man. John Wiley & Sons, Inc., New York, 1957.
9. Ben-Akiva M. K. Simulation Laboratory for Evaluating Dynamic Traffic Management Systems. ASCE Journal of Transportation Engineering, Vol. 123, No. 4, 1997, pp. 283–289.
10. Myers J. L., and Sadler E. Effects of Range of Payoffs as a Variable in Risk Taking. Journal of Experimental Psychology, Vol. 60, No. 5, 1960, pp. 306–309.
11. Busemeyer J. R., and Townsend J. T. Decision Field Theory: A Dynamic Cognitive Approach to Decision Making. Psychological Review, Vol. 100, No. 3, 1993, pp. 432–459.
12. Erev I., and Barron G. On Adaptation, Maximization, and Reinforcement Learning Among Cognitive Strategies. Psychological Review, Vol. 112, No. 4, 2005, pp. 912–931.
13. Avineri E., and Prashker J. N. Sensitivity to Uncertainty: Need for a Paradigm Shift. In Transportation Research Record: Journal of the Transportation Research Board, No. 1854, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 90–98.
14. Denrell J., and March J. G. Adaptation as Information Restriction: The Hot Stove Effect. Organizational Science, Vol. 12, No. 5, 2001, pp. 523–538.
15. Denrell J. Adaptive Learning and Risk Taking. Psychological Review, Vol. 114, No. 1, 2007, pp. 177–187.
16. Abdel-Aty M. A. Using Stated Preferences Data for Studying the Effect of Advanced Traffic Information on Drivers’ Route Choice. Transportation Research Part C, Vol. 5, No. 1, 1997, pp. 39–50.
17. Kaptein N. A., Theeuwes J., and van der Horst R. Driving Simulator Validity: Some Considerations. In Transportation Research Record 1550, TRB, National Research Council, Washington, D.C., 1996, pp. 30–36.

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Article first published online: January 1, 2011
Issue published: January 2011

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© 2011 National Academy of Sciences.
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Authors

Affiliations

Gautam Divekar
Department of Mechanical and Industrial Engineering, Arbella Insurance Human Performance Laboratory, Amherst, Amherst, MA 01003.
Hasmik Mehranian
Department of Mechanical and Industrial Engineering, Arbella Insurance Human Performance Laboratory, Amherst, Amherst, MA 01003.
Matthew R. E. Romoser
Department of Mechanical and Industrial Engineering, Arbella Insurance Human Performance Laboratory, Amherst, Amherst, MA 01003.
Jeffrey W. Muttart
Department of Mechanical and Industrial Engineering, Arbella Insurance Human Performance Laboratory, Amherst, Amherst, MA 01003.
Per Garder
Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469.
John Collura
Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, Amherst, MA 01003.
Donald L. Fisher
Department of Mechanical and Industrial Engineering, Arbella Insurance Human Performance Laboratory, Amherst, Amherst, MA 01003.

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