Travelers' day-to-day route choice behavior with real-time information in a congested risky network
Lu, X., Gao, S., Ben-Elia, E. and Pothering, R. (2014) Travelers' day-to-day route choice behavior with real-time information in a congested risky network. Mathematical Population Studies, 21 (4). pp. 205-219. ISSN 0889-8480
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Publisher's URL: http://dx.doi.org/10.1080/08898480.2013.836418
Non-recurring 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.
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