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Modeling Learning Impacts on Day-to-day Travel Choice

  • Ozlem Yanmaz-Tuzel
  • Kaan Ozbay
Chapter

Abstract

This paper uses Stochastic Learning Automata and Bayesian Inference theory to model drivers’ day-to-day learning behavior in an uncertain environment. The proposed model addresses the adaptation of travelers on the basis of experienced choices and user-specific characteristics. Using the individual commuter data obtained from New Jersey Turnpike, the parameters of the model are estimated. The proposed model aims to capture the commuters’ departure time choice learning/adaptation behavior under disturbed network conditions (after toll change), and to investigate commuters’ responses to toll, travel time, departure/arrival time restrictions while selecting their departure times. The results have demonstrated the possibility of developing a psychological framework (i.e., learning models) as a viable approach to represent travel behavior.

Keywords

Learning Behavior Road Section Learn Automaton Transportation Research Record Transportation Research Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Avineri, E. and Prashker, J.N. (2003). Sensitivity to uncertainty: the need for a paradigm shift. Transportation Research Record, 1854, 90-98.CrossRefGoogle Scholar
  2. Arentze, T. and Timmermans, H. (2003). Modeling learning and adaptation processes in activity-travel choice: a framework and numerical experiment. Transportation, 30, 37-62.CrossRefGoogle Scholar
  3. Bogers, E.A.I., Bierlaire, M. and Hoogendoorn, S.P. (2007). Modeling learning in route choice. Transportation Research Record, 2014, 1-8.CrossRefGoogle Scholar
  4. Ben-Akiva, M.E., De Palma, A. and Kaysi, I. (1991). Dynamic network models and driver information systems. Transportation Research Part A, 25, 251-266.CrossRefGoogle Scholar
  5. Chen, R.B. and Mahmassani, H.S. (2004). Travel time perception and learning mechanisms in traffic networks. Transportation Research Record, 1894, 209-221.CrossRefGoogle Scholar
  6. Erev, I. and Roth, A.E. (1998). Predicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 88, 848-881.Google Scholar
  7. Erev, I., Bereby-Meyer, Y. and Roth, A. (1999). The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models. Journal of Economic Behavior & Organization, 39, 111-128.CrossRefGoogle Scholar
  8. Ettema, D., Timmermans, H. and Arentze, T. (2004). Modeling perception updating of travel times in the context of departure time choice under ITS. ITS Journal, 8, 33-43.Google Scholar
  9. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin D.B. (2003). Posterior simulation. Bayesian Data Analysis,291-292.Google Scholar
  10. Heidelberger, P. and Welch, P. (1983). Simulation run length control in the presence of an initial transient. Operations Research, 31, 1109-1144.CrossRefGoogle Scholar
  11. Horowitz, J.L. (1984). The stability of stochastic equilibrium in a two-link transportation network. Transportation Research Part B, 18, 13-28.CrossRefGoogle Scholar
  12. Jha, M., Madanat, S. and Peeta, S. (1998). Perception updating and day-to-day travel choice dynamics in traffic networks with information provision. Transportation Research Part C, 6, 189-212.CrossRefGoogle Scholar
  13. Jotisankasa, A. and Polak, J.W. (2005). Modeling learning and adaptation in route and departure time choice behavior: achievements and prospects. Integrated Land-Use and Transportation Models.Elsevier, Oxford, United Kingdom.Google Scholar
  14. Jotisankasa, A. and Polak, J.W. (2006). Framework for travel time learning and behavioral adaptation in route and departure time choice. Transportation Research Record, 1985, 231-240.CrossRefGoogle Scholar
  15. Kahneman, D. and Tversky, A. (1979). Prospect theory: an analysis of decisions under risk. Econometrica, 47, 263-291.CrossRefGoogle Scholar
  16. Kobayashi, K. (1994). Information, rational expectations, and network equilibria: an analytical perspective for route guidance systems. Annals of Regional Science, 28, 369-393.CrossRefGoogle Scholar
  17. Mahmassani, H.S. and Chang, G.L. (1985). Dynamic aspects of departure time choice behavior in commuting system: theoretical framework and experimental analysis. Transportation Research Record, 1037, 88-101.Google Scholar
  18. March, J.G. (1996). Learning to be risk averse. Psychological Review, 10, 309-319.CrossRefGoogle Scholar
  19. Miyagi, T. (2005). A reinforcement learning model for simulating route choice behaviors in transport network. Proceedings of 16th Mini - EURO Conference and 10th Meeting of EWGT.Google Scholar
  20. Nakayama, S., Kitamura, R. and Fujii, S. (2001). Drivers’ route choice rules and network behavior: do drivers become rational and homogeneous through learning? Transportation Research Record, 1752, 62-68.CrossRefGoogle Scholar
  21. Narendra, K.S. and Thathachar, M.A.L. (1898). Learning Automata. Englewood Cliffs, N.J.: Prentice-Hall, Inc.Google Scholar
  22. Ozbay, K., Datta, A. and Kuchroo, P. (2001). Modeling route choice behavior using SLA. Transportation Research Record, 1752, 38-46.CrossRefGoogle Scholar
  23. Ozbay, K., Datta, A. and Kuchroo, P. (2002). Application of stochastic learning automata for modeling departure time and route choice behavior. Transportation Research Record, 1807, 154-162.CrossRefGoogle Scholar
  24. Ozbay, K., Holguín-Veras, J., Yanmaz-Tuzel, O., Mudigonda, S., Lichtenstein, A., Robins, M., Bartin, B., Cetin, M., Xu, N., Zorrilla, J.C., Xia, S., Wang, S. and Silas, M. (2005). Evaluation Study of New Jersey Turnpike Authority’s Time-of-day Pricing Initiative. FHWA-NJ-2005-012.FHWA, U.S. Department of Transportation.Google Scholar
  25. Ozbay, K. and Yanmaz-Tuzel, O. (2006). Modeling of commuters’ day-to-day learing behavior. Proceedings of First International Symposium on Dynamic Traffic Assignment.Google Scholar
  26. Ozbay, K., Yanmaz-Tuzel, O. and Holguin-Veras, J. (2006). Evaluation of combined traffic impacts of time-of-day pricing program and E-ZPass usage on New Jersey Turnpike. Transportation Research Record, 1960, 40-47.CrossRefGoogle Scholar
  27. Ramming, M.S. (2002). Network Knowledge and Route Choice. PhD thesis, Department of Civil Engineering, Massachusetts Institute of Technology.Google Scholar
  28. Roth, A.E. and Erev, I. (1995). Learning in extensive-form games: experimental data and simple dynamic models in intermediate term, games and economic Behavior. Nobel Symposium, 8, 164-212.Google Scholar
  29. Schreckenberg, M. and Selten, R. (2004). Experimental investigation of day-to-day route-choice behavior and network simulations of autobahn traffic in North Rhine-Westphalia. Human Behaviour and Traffic Networks, 1, 1-22.Google Scholar
  30. Selten, R., Schreckenberg, M., Pitz , T., Chmura, T. and Kube, S. (2007). Commuters route choice behavior. Games and Economic Behavior, 58, 394-406.CrossRefGoogle Scholar
  31. Senbil, M. and Kitamura, R. (2004). Reference points in commuter departure time choice: a prospect theoretic test of alternative decision frames. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 8, 19-31.Google Scholar
  32. Simon, H.A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99-118.CrossRefGoogle Scholar
  33. Sutton, R.S. and Barto, S. (1998). Reinforcement Learning. Cambridge, MA: MIT Press, 1998.Google Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Ozlem Yanmaz-Tuzel
    • 1
  • Kaan Ozbay
    • 1
  1. 1.Rutgers UniversityLondonU.S.A

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