Modeling Learning Impacts on Day-to-day Travel Choice

This paper uses Stochastic Learning Automata and Bayesian Inference theory to model drivers' day-to-day learning behavior in an uncertain environment. The proposed model addresses the adaptation of travelers on the basis of experienced choices and user-specific characteristics. Using the individual commuter data obtained from New Jersey Turnpike, the parameters of the model are estimated. The proposed model aims to capture the commuters' departure time choice learning/adaptation behavior under disturbed network conditions (after toll change), and to investigate commuters' responses to toll, travel time, departure/arrival time restrictions while selecting their departure times. The results have demonstrated the possibility of developing a psychological framework (i.e., learning models) as a viable approach to represent travel behavior.