In two experiments, participants chose between staying on a main route with a certain travel time and diverting to an alternative route that could take a range of travel times. In the first experiment, travel time information was displayed on a sheet of paper to participants seated at a desk. In the second experiment, the same information was displayed in a virtual environment through which participants drove. Overall, participants were risk-averse when the average travel time along the alternative route was shorter than the certain travel time of the main route but risk-seeking when the average travel time of the alternative route was longer than the certain travel time along the main route. In the second experiment, in which cognitive load was higher, participants simplified their decision-making strategies. A simple probabilistic model describes the risk-taking behavior and the load effects. Actual or potential applications of this research include the development of efficient travel time information systems for drivers.
Get full access to this article
View all access options for this article.
References
1.
Allen, T. M., Lunenfeld, H., & Alexander, G. J. (1971). Driver information needs. Highway Research Record, 366, 102–115.
2.
Abdel-Aty, M., Kitamura, R., & Jovanis, P. (1997). Using stated preference data for studying the effect of advanced traffic-information on drivers' route choice. Transportation Research C, 5(1), 39–50.
3.
Abdel-Aty, M., Kitamura, R., Jovanis, P., & Vaughn, K. M. (1994). Investigation of criteria influencing route choice: Initial analysis using revealed and stated preference data (Research report UCD-ITS-RR-94-12). Davis: University of California, Institute of Transportation Studies.
4.
Abdel-Aty, M., Vaughn, K. M., Kitamura, R., Jovanis, P., & Mannering, F. L. (1994). Models of commuters' information use and route choice: Initial results based on Southern California commuter route choice survey. Transportation Research Record, 1453, 46–55.
5.
Ben-Akiva, M., & Lerman, S. R. (1997). Discrete choice analysisCambridge: MIT Press.
6.
Bovy, P. H. L., & Stern, E. (1990). Route choice: Wayfinding in transportation networksBoston: Kluwer Academic.
7.
Coombs, C. H., Dawes, R. M., & Tversky, A. (1970). Mathematical psychology: An elementary introductionEnglewood Cliffs, NJ: Prentice-Hall.
8.
Holton, A., & Fisher, D. L. (1998). Advanced parking management systems: Modeling drivers' parking decision strategies. In Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting (pp. 1237–1241). Santa Monica, CA: Human Factors and Ergonomics Society.
9.
Huchingson, R., McNees, R., & Dudek, C. (1977). Survey of motorist route-selection criteria. Transportation Research Record, 643, 45–48.
10.
Huynh, H., & Feldt, L. S. (1970). Conditions under which mean square ratios in repeated measurements designs have exact F distributions. Journal of the American Statistical Association, 65, 1582–1589.
11.
Huynh, H., & Feldt, L. S. (1976). Estimation of the Box correction for degrees of freedom from sample data in randomized block and split-plot designs. Journal of Educational Statistics, 1, 69–82.
12.
Jackson, W., & Jucker, J. (1981). An empirical study of travel time variability and travel choice behavior. Transportation Science, 16(4), 460–475.
13.
Jovanis, P. (1993). Modeling travel behavior with advanced traveler information. Transportation, 20, 79–82.
14.
Kahn, A. M. (1992). Technological response to urban traffic congestion. Journal of Urban Technology, Fall, 19–28.
15.
Kaptein, N. A., Theeuwes, J., & van der Horst, R. (1995). Driving simulator validity: Some considerations. Transportation Research Record, 1550, 30–36.
16.
Lerner, N., Huey, R., Zador, P., Harpster, J., & Douncan, D. (1998). User preferences for information types in advanced traveler information system applications. In Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting (pp. 1200–1204). Santa Monica, CA: Human Factors and Ergonomics Society.
17.
Myers, J. L., DiCecco, J. V., White, J. B., & Borden, V. M. (1982). Repeated measurements on dichotomous variables: Q and F tests. Psychological Bulletin, 92, 517–525.
18.
Paine, F. T., Nash, A. N., Hille, S. J., & Brunner, G. A. (1967).Consumer conceived attitudes of transportation: An attitude study (clearinghouse ed.). College Park: University of Maryland.
19.
Polydoropoulou, A., Ben-Akiva, M., & Kaysi, I. (1994). Influence of traffic information on drivers' route choice behavior. Transportation Research Record, 1453, 56–65.
20.
Szymkowiak, A., Fisher, D. L., & Connerney, K. A. (1997). False yield and false go decisions at signalized left-turn intersections: A driving simulator study. In Proceedings of the Europe Chapter of the Human Factors and Ergonomics Society Annual Conference (pp. 226–235). Bochum, Germany: Fachhochschule Bochum, University of Applied Sciences.
21.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453–458.
22.
University of Maryland.(1966). User determined attributes of ideal transportation system: An empirical study (prepared for U.S. Department of Commerce, Bureau of Public Roads). College Park, MD: Author.
23.
Zhao, S., Muromachi, Y., Harata, N., & Ohta, K. (1995). A SP model for route choice behavior in response to travel time information with marginal errors. In Proceedings of the Seventh Western Council for Travel Research Meetings (pp.41–54). Sydney, Australia: Western Council for Travel Research.
