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Empirical analysis of scaled mixed itinerary-size weibit model for itinerary choice in a schedule-based railway network

  • Keyu Wen
  • , Jiemin Xie*
  • , Anthony Chen
  • , S. C. Wong
  • , Shuguang Zhan
  • , S. M. Lo
  • , Lixia Qiang
    • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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Abstract

The mixed itinerary-size weibit (MISW) model was recently developed for predicting passengers’ itinerary-choice behaviors in a schedule-based railway network. It considers passengers’ heterogeneous perceptions and relaxes the independently and identically distributed assumptions of random utility models. However, this model has not been verified using real-world data. Moreover, it is assumed that passengers hold a negative perception of overlapping, but this assumption may not be suitable for all situations. Thus, this study proposes a scaled MISW model which includes a scale parameter to address this issue. We collected passenger ticket-booking data from the South China High-Speed Railway network and conducted an empirical analysis in which we compared the performances of the scaled MISW model and other models (i.e. the multinomial logit, multinomial weibit, and MISW models). According to the results, the scaled MISW model outperformed the other models in describing passengers’ choice behaviors in the railway network.
Original languageEnglish
Pages (from-to)934-962
JournalTransportmetrica A: Transport Science
Volume18
Issue number3
Online published14 Apr 2021
DOIs
Publication statusPublished - 2022

Research Keywords

  • empirical analysis
  • high-speed railway
  • itinerary choice
  • Scaled mixed itinerary-size weibit model

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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    • TBRS: Safety, Reliability, and Disruption Management of High Speed Rail and Metro Systems

      XIE, M. (Principal Investigator / Project Coordinator), BENSOUSSAN, A. (Co-Principal Investigator), LO, S. M. (Co-Principal Investigator), SHOU, B. (Co-Principal Investigator), SINGPURWALLA, N. D. (Co-Principal Investigator), TSE, W. T. P. (Co-Principal Investigator), TSUI, K. L. (Co-Principal Investigator), YU, Y. (Co-Principal Investigator), YUEN, K. K. R. (Co-Principal Investigator), CHAN, A. B. (Co-Investigator), CHAN, N.-H. (Co-Investigator), CHIN, K. S. (Co-Investigator), CHOW, H. A. (Co-Investigator), Chow, W. K. (Co-Investigator), EDESESS, M. (Co-Investigator), GOLDSMAN, D. M. (Co-Investigator), Huang, J. (Co-Investigator), LEE, W. M. (Co-Investigator), LI, L. (Co-Investigator), LI, C. L. (Co-Investigator), LING, M. H. A. (Co-Investigator), LIU, S. (Co-Investigator), MURAKAMI, J. (Co-Investigator), NG, S. Y. S. (Co-Investigator), NI, M. C. (Co-Investigator), TAN, M.H.-Y. (Co-Investigator), Wang, W. (Co-Investigator), Wang, J. (Co-Investigator), WONG, C. K. (Co-Investigator), WONG, S. Y. Z. (Co-Investigator), WONG, S. C. (Co-Investigator), Xu, Z. (Co-Investigator), ZHANG, Z. (Co-Investigator), Zhang, D. (Co-Investigator), ZHAO, J. L. (Co-Investigator) & Zhou, Q. (Co-Investigator)

      1/01/16 → 31/12/21

      Project: Research

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