|Title||Travelers’ day-to-day route choice behavior with real-time information in a congested risky network|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Lu X, Gao S, Ben-Elia E, Pothering R|
|Journal||Mathematical Population Studies|
|Keywords||experiment, real-time information, reinforcement learning, uncertain network|
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.