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Bayesian Learning, Day-to-day Adjustment Process, and Stability of Wardrop Equilibrium

In: Transportation and Traffic Theory 2009: Golden Jubilee

Author

Listed:
  • Shoichiro Nakayama

    (Kanazawa University, Japan and University of Leeds)

Abstract

In this study, we assume that drivers under day-to-day dynamic transportation circumstances choose routes based on Bayesian learning and develop a day-to-day dynamic model of network flow. This model reveals that a driver using Bayesian learning chooses the route that frequently takes the minimum travel time. Furthermore, we find that the equilibrium point of the day-to-day dynamic model is identical to Wardrop’s equilibrium. Under complete information (when information about which route takes the minimum travel time is given after the trips), Wardrop’s equilibrium is globally asymptotically stable and the day-to-day dynamic system converges to Wardrop’s equilibrium if initial recognition among drivers is distributed widely. Under incomplete information, Wardrop’s equilibrium is always globally asymptotically stable regardless of what the drivers’ initial recognition is. Paradoxically, the condition for stable equilibrium under incomplete information is more relaxed than that under complete information.

Suggested Citation

  • Shoichiro Nakayama, 2009. "Bayesian Learning, Day-to-day Adjustment Process, and Stability of Wardrop Equilibrium," Springer Books, in: William H. K. Lam & S. C. Wong & Hong K. Lo (ed.), Transportation and Traffic Theory 2009: Golden Jubilee, chapter 0, pages 425-440, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-0820-9_21
    DOI: 10.1007/978-1-4419-0820-9_21
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    Citations

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    Cited by:

    1. Tatsuya Iwase & Yukihiro Tadokoro & Daisuke Fukuda, 2017. "Self-Fulfilling Signal of an Endogenous State in Network Congestion Games," Networks and Spatial Economics, Springer, vol. 17(3), pages 889-909, September.
    2. Zhu, Zheng & Mardan, Atabak & Zhu, Shanjiang & Yang, Hai, 2021. "Capturing the interaction between travel time reliability and route choice behavior based on the generalized Bayesian traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 48-64.
    3. Parry, Katharina & Hazelton, Martin L., 2013. "Bayesian inference for day-to-day dynamic traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 104-115.

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