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Complexity
Volume 2018, Article ID 3412070, 14 pages
https://doi.org/10.1155/2018/3412070
Research Article

A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2Department of Civil, Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, USA

Correspondence should be addressed to Kai Lu; moc.361@utjb_iakul and Baoming Han; nc.ude.utjb@nahmb

Received 18 December 2017; Revised 2 June 2018; Accepted 15 July 2018; Published 28 August 2018

Academic Editor: Shuliang Wang

Copyright © 2018 Kai Lu 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.

Abstract

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.