@article { , title = {Travelers’ Day-to-Day Route Choice Behavior with Real-Time Information in a Congested Risky Network}, abstract = {© 2014, Taylor \& Francis Group, LLC. 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.}, doi = {10.1080/08898480.2013.836418}, eissn = {1547-724X}, issn = {0889-8480}, issue = {4}, journal = {Mathematical Population Studies}, pages = {205-219}, publicationstatus = {Published}, publisher = {Taylor \& Francis (Routledge)}, url = {https://uwe-repository.worktribe.com/output/825441}, volume = {21}, keyword = {Centre for Transport and Society, experiment, uncertain network, reinforcement learning, real-time information}, year = {2014}, author = {Lu, Xuan and Gao, Song and Ben-Elia, Eran and Pothering, Ryan} }