Learning the route choice behavior of subway passengers from AFC data

This paper learns the route choice behavior of passengers from Auto Fare Collection, timetable, and train loading data using a method combined with Bayesian inference and Metropolis-Hasting sampling. First, the influential factors of route choice such as in-vehicle travel time, transfer time, and in-vehicle crowding are given. Next, formulations are established based on AFC, timetable and train loading data, which are merged into a logit model of route choice behavior of subway passengers. Next, an algorithm integrating Bayesian inference and Metropolis-Hasting sampling is designed to calibrate parameters of the logit model. Finally, a case study of Beijing subway is applied to verify the validity of the model and algorithm. A detailed discussion shows that in-vehicle crowding plays a crucial role in passenger route choice behavior.