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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 5824051, 12 pages
Research Article

Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

1College of Transportation Engineering, Tongji University, Shanghai 201804, China
2School of Mathematical Sciences, Tongji University, Shanghai 200092, China
3College of Maritime and Transportation, Ningbo University, Ningbo 315211, China

Correspondence should be addressed to Wei Zhu

Received 20 March 2017; Revised 14 June 2017; Accepted 5 July 2017; Published 15 August 2017

Academic Editor: Wai Yuen Szeto

Copyright © 2017 Wei Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An urban rail transit (URT) system is operated according to relatively punctual schedule, which is one of the most important constraints for a URT passenger’s travel. Thus, it is the key to estimate passengers’ train choices based on which passenger route choices as well as flow distribution on the URT network can be deduced. In this paper we propose a methodology that can estimate individual passenger’s train choices with real timetable and automatic fare collection (AFC) data. First, we formulate the addressed problem using Manski’s paradigm on modelling choice. Then, an integrated framework for estimating individual passenger’s train choices is developed through a data-driven approach. The approach links each passenger trip to the most feasible train itinerary. Initial case study on Shanghai metro shows that the proposed approach works well and can be further used for deducing other important operational indicators like route choices, passenger flows on section, load factor of train, and so forth.