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Travelers' Day-to-Day Route Choice Behavior with Real-Time Information in a Congested Risky Network

Author

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  • XUAN LU
  • SONG GAO
  • ERAN BEN-ELIA
  • RYAN POTHERING

Abstract

Nonrecurring disruptions to traffic systems caused by incidents or adverse conditions can result in uncertain travel times. Real-time information allows travelers to adapt to actual traffic conditions. In a behavior experiment, subjects completed 120 "days" of repeated route choices in a hypothetical, competitive network submitted to random capacity reductions. One scenario provided subjects with real-time information regarding a probable incident and the other did not. A reinforcement learning model with two scale factors, a discounting rate of previous experience and a constant term, is estimated by minimizing the deviation between predicted and observed daily flows. The estimation combines brute force enumeration and a subsequent stochastic approximation method. The prediction over 120 runs has a root mean square error of 1.05 per day per route and a bias of 0.14 per route.

Suggested Citation

  • Xuan Lu & Song Gao & Eran Ben-Elia & Ryan Pothering, 2014. "Travelers' Day-to-Day Route Choice Behavior with Real-Time Information in a Congested Risky Network," Mathematical Population Studies, Taylor & Francis Journals, vol. 21(4), pages 205-219, December.
    • Handle: RePEc:taf:mpopst:v:21:y:2014:i:4:p:205-219
    DOI: 10.1080/08898480.2013.836418
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    References listed on IDEAS

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    1. Ben-Elia, Eran & Shiftan, Yoram, 2010. "Which road do I take? A learning-based model of route-choice behavior with real-time information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 249-264, May.
    2. Selten, R. & Chmura, T. & Pitz, T. & Kube, S. & Schreckenberg, M., 2007. "Commuters route choice behaviour," Games and Economic Behavior, Elsevier, vol. 58(2), pages 394-406, February.
    3. Erel Avineri & Joseph Prashker, 2006. "The Impact of Travel Time Information on Travelers’ Learning under Uncertainty," Transportation, Springer, vol. 33(4), pages 393-408, July.
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    2. Kasun P Wijayaratna & Vinayak V Dixit & Laurent Denant-Boemont & S Travis Waller, 2017. "An experimental study of the Online Information Paradox: Does en-route information improve road network performance?," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-17, September.
    3. Levy, Nadav & Klein, Ido & Ben-Elia, Eran, 2018. "Emergence of cooperation and a fair system optimum in road networks: A game-theoretic and agent-based modelling approach," Research in Transportation Economics, Elsevier, vol. 68(C), pages 46-55.
    4. Collins, Mor & Etzioni, Shelly & Ben-Elia, Eran, 2024. "Travel behavior and system dynamics in a simple gamified automated multimodal network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    5. Jiang, Gege & Fosgerau, Mogens & Lo, Hong K., 2020. "Route choice, travel time variability, and rational inattention," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 188-207.
    6. Tang, Yue & Gao, Song & Ben-Elia, Eran, 2017. "An exploratory study of instance-based learning for route choice with random travel times," Journal of choice modelling, Elsevier, vol. 24(C), pages 22-35.
    7. Rapoport, Amnon & Qi, Hang & Mak, Vincent & Gisches, Eyran J., 2019. "When a few undermine the whole: A class of social dilemmas in ridesharing," Journal of Economic Behavior & Organization, Elsevier, vol. 166(C), pages 125-137.

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