Learning the route choice behavior of subway passengers from AFC data

• Published in: Expert systems with applications 2018-04, Vol.95, p.324-332
• Main Authors: Xu, Xinyue, Xie, Liping, Li, Haiying, Qin, Lingqiao
• Format: Article
• Language: English
• Subjects: AFC; Algorithms; Bayesian; Bayesian analysis; Big data; Crowding; Formulations; In vehicle; Passengers; Route choice; Route choice behavior; Route selection; Sampling; Statistical inference; Subway
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.11.043

•Route choice behavior is learned from Auto Fare Collection, timetable and train loading data.•The influence of in-vehicle crowding on route choice behavior is explicitly considered.•Parameters of the route choice model are calibrated using the Bayesian and Metropolis-Hasting sampling method.•The proposed data mining method outperforms three competing methods in terms of accuracy. 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.