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Mathematical Problems in Engineering
Volume 2014, Article ID 646548, 19 pages
http://dx.doi.org/10.1155/2014/646548
Research Article

A Day-to-Day Route Choice Model Based on Reinforcement Learning

College of Management and Economic, Tianjin University, Tianjin 300072, China

Received 10 April 2014; Accepted 28 August 2014; Published 30 September 2014

Academic Editor: X. Zhang

Copyright © 2014 Fangfang Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. He, X. Guo, and H. X. Liu, “A link-based day-to-day traffic assignment model,” Transportation Research B: Methodological, vol. 44, no. 4, pp. 597–608, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Watling and M. L. Hazelton, “The dynamics and equilibria of day-to-day assignment models,” Networks and Spatial Economics, vol. 3, no. 3, pp. 349–370, 2003. View at Google Scholar
  3. M. J. Smith, “The stability of a dynamic model of traffic assignment—an application of a method of Lyapunov,” Transportation Science, vol. 18, no. 3, pp. 245–252, 1984. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. T. L. Friesz, D. Bernstein, N. J. Mehta, R. L. Tobin, and S. Ganjalizadeh, “Day-to-day dynamic network disequilibria and idealized traveler information systems,” Operations Research, vol. 42, no. 6, pp. 1120–1136, 1994. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. C. F. Daganzo and Y. Sheffi, “On stochastic models of traffic assignment,” Transportation Science, vol. 11, no. 3, pp. 253–274, 1977. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Cascetta, “A stochastic process approach to the analysis of temporal dynamics in transportation networks,” Transportation Research B, vol. 23, no. 1, pp. 1–17, 1989. View at Google Scholar · View at Scopus
  7. F. Yang and H. X. Liu, “A new modeling framework for travelers day-to-day route choice adjustment processes,” Transportation and Traffic Theory 2007. Papers Selected for Presentation at ISTTT17, 2007.
  8. K. Parry and M. L. Hazelton, “Bayesian inference for day-to-day dynamic traffic models,” Transportation Research B: Methodological, vol. 50, pp. 104–115, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Watling, “Stability of the stochastic equilibrium assignment problem: a dynamical systems approach,” Transportation Research B: Methodological, vol. 33, no. 4, pp. 281–312, 1999. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Watling, “Urban traffic network models and dynamic driver information systems,” Transport Reviews, vol. 14, no. 3, pp. 219–246, 1994. View at Google Scholar
  11. R. J. F. Rossetti, R. H. Bordini, A. L. C. Bazzan, S. Bampi, R. Liu, and D. V. Vliet, “Using BDI agents to improve driver modelling in a commuter scenario,” Transportation Research C: Emerging Technologies, vol. 10, no. 5-6, pp. 373–398, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Nakayama, R. Kitamura, and S. Fujii, “Drivers' learning and network behavior dynamic analysis of the driver-network system as a complex system,” Transportation Research Record, vol. 1676, no. 1, pp. 30–36, 1999. View at Google Scholar · View at Scopus
  13. S. Nakayama and R. Kitamura, “Route choice model with inductive learning,” Transportation Research Record, vol. 1725, no. 1, pp. 63–70, 2000. View at Google Scholar · View at Scopus
  14. S. Nakayama, R. Kitamura, and S. Fujii, “Drivers’ route choice rules and network behavior: do drivers become rational and homogeneous through learning?” Transportation Research Record, no. 1752, pp. 62–68, 2001. View at Google Scholar · View at Scopus
  15. M. Jha, S. Madanat, and S. Peeta, “Perception updating and day-to-day travel choice dynamics in traffic networks with information provision,” Transportation Research C: Emerging Technologies, vol. 6, no. 3, pp. 189–212, 1998. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Chen and H. S. Mahmassani, “Travel time perception and learning mechanisms in traffic networks,” Transportation Research Record, vol. 1894, no. 1, pp. 209–221, 2004. View at Google Scholar · View at Scopus
  17. F. Klügl and A. L. C. Bazzan, “Route decision behaviour in a commuting scenario: simple heuristics adaptation and effect of traffic forecast,” Journal of Artificial Societies and Social Simulation, vol. 7, no. 1, 2004. View at Google Scholar · View at Scopus
  18. A. L. C. Bazzan and F. Klugl, “Learning to behave socially and avoid the braess paradox in a commuting scenario,” in Proceedings of the 1st International Workshop on Evolutionary Game Theory for Learning in MAS, 2003.
  19. H. Dia, “An agent-based approach to modelling driver route choice behaviour under the influence of real-time information,” Transportation Research C: Emerging Technologies, vol. 10, no. 5-6, pp. 331–349, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. T.-L. Liu and H.-J. Huang, “Multi-agent simulation on day-to-day route choice behavior,” in Proceeding of the 3rd International Conference on Natural Computation (ICNC ߣ07), vol. 5, pp. 492–498, Haikou, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Illenberger, G. Flotterod, and K. Nagel, “A model of risk-sensitive route-choice behavior and the potential benefit of route guidance,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 384–389, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. F. Gao and M.-Z. Wang, “Route choice behavior model with guidance information,” Journal of Transportation Systems Engineering and Information Technology, vol. 10, no. 6, pp. 64–69, 2010. View at Google Scholar · View at Scopus
  23. R. Lahkar and R. M. Seymour, “Reinforcement learning in population games,” Games and Economic Behavior, vol. 80, pp. 10–38, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. J. G. Cross, “A stochastic learning model of economic behavior,” The Quarterly Journal of Economics, vol. 87, no. 2, pp. 239–266, 1973. View at Google Scholar
  25. E. Ben-Elia and Y. Shiftan, “Which road do I take? A learning-based model of route-choice behavior with real-time information,” Transportation Research A: Policy and Practice, vol. 44, no. 4, pp. 249–264, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. M. M. Wahba, Microsimulation Learning-based Approach to Transit Assignment, University of Toronto, 2008.
  27. M. Wahba and A. Shalaby, “Large-scale application of MILATRAS: case study of the Toronto transit network,” Transportation, vol. 38, no. 6, pp. 889–908, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Wahba and A. Shalaby, “Learning-based framework for transit assignment modeling under information provision,” Transportation, vol. 41, no. 2, pp. 397–417, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Zolfpour-Arokhlo, A. Selamat, S. Z. Mohd Hashim, and H. Afkhami, “Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms,” Engineering Applications of Artificial Intelligence, vol. 29, pp. 163–177, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. M. W. Macy and A. Flache, “Learning dynamics in social dilemmas,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, supplement 3, pp. 7229–7236, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Lasaulce and H. Tembine, Game Theory and Learning for Wireless Networks: Fundamentals and Applications, Academic Press, 2011.
  32. H. Xu, Y. Lou, Y. Yin, and J. Zhou, “A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing,” Transportation Research B: Methodological, vol. 45, no. 2, pp. 311–328, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. X. Guo and H. X. Liu, “Bounded rationality and irreversible network change,” Transportation Research B: Methodological, vol. 45, no. 10, pp. 1606–1618, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. Q. Wang and W. Xu, “A user equilibrium model based on cumulative prospect theory for degradable transport network,” in Proceeding of the 4th International Joint Conference on Computational Sciences and Optimization (CSO ߣ11), pp. 1078–1082, Yunnan, China, April 2011. View at Publisher · View at Google Scholar · View at Scopus