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

• Published in: Mathematical population studies Vol. 21; no. 4; pp. 205 - 219
• Main Authors: LU, XUAN, GAO, SONG, BEN-ELIA, ERAN, POTHERING, RYAN
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 02.10.2014; Taylor & Francis Ltd
• Subjects: Behavioral decision theory; Cognitive models; experiment; Network flow problem; Real time; real-time information; reinforcement learning; Route optimization; Traffic congestion; uncertain network
• ISSN: 0889-8480; 1547-724X
• DOI: 10.1080/08898480.2013.836418

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.