Learning the route choice behavior of subway passengers from AFC data

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Subway,Big data,Route choice behavior,Bayesian,Crowding,AFC

论文评审过程：Received 16 May 2017, Revised 31 October 2017, Accepted 21 November 2017, Available online 22 November 2017, Version of Record 1 December 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.11.043