Travelers' Day-to-Day Route Choice Behavior with Real-Time Information in a Congested Risky Network |
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Authors: | XUAN LU SONG GAO ERAN BEN-ELIA RYAN POTHERING |
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Institution: | 1. Department of Civil and Environmental Engineering , University of Massachusetts Amherst , Massachusetts , USA luxuan0310@gmail.com;3. Department of Civil and Environmental Engineering , University of Massachusetts Amherst , Massachusetts , USA;4. Department of Geography and Environmental Development , Ben-Gurion University of the Negev , Beersheva , Israel |
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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. |
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Keywords: | experiment real-time information reinforcement learning uncertain network |
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