A trip purpose-based data-driven alighting station choice model using transit smart card data

Kai Lu, Alireza Khani, Baoming Han

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article number3412070
StatePublished - 2018

Bibliographical note

Funding Information:
This work was supported by the Foundation of China Scholarship Council, the National Natural Science Foundation Project of P.R. China under Grant no. U1434207, and the Beijing Municipal Natural Science Foundation under Reference no. 8162033. The authors thank the coworkers in CEGE transit lab at University of Minnesota for their constructive suggestions and comments that have led to a significant improvement in this paper.

Publisher Copyright:
© 2018 Kai Lu et al.


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