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Metro passengers’ route choice model and its application considering perceived transfer threshold

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  • Fanglei Jin
  • Enjian Yao
  • Yongsheng Zhang
  • Shasha Liu

Abstract

With the rapid development of the Metro network in China, the greatly increased route alternatives make passengers’ route choice behavior and passenger flow assignment more complicated, which presents challenges to the operation management. In this paper, a path sized logit model is adopted to analyze passengers’ route choice preferences considering such parameters as in-vehicle time, number of transfers, and transfer time. Moreover, the “perceived transfer threshold” is defined and included in the utility function to reflect the penalty difference caused by transfer time on passengers’ perceived utility under various numbers of transfers. Next, based on the revealed preference data collected in the Guangzhou Metro, the proposed model is calibrated. The appropriate perceived transfer threshold value and the route choice preferences are analyzed. Finally, the model is applied to a personalized route planning case to demonstrate the engineering practicability of route choice behavior analysis. The results show that the introduction of the perceived transfer threshold is helpful to improve the model’s explanatory abilities. In addition, personalized route planning based on route choice preferences can meet passengers’ diversified travel demands.

Suggested Citation

  • Fanglei Jin & Enjian Yao & Yongsheng Zhang & Shasha Liu, 2017. "Metro passengers’ route choice model and its application considering perceived transfer threshold," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-17, September.
    • Handle: RePEc:plo:pone00:0185349
    DOI: 10.1371/journal.pone.0185349
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    References listed on IDEAS

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    1. Raveau, Sebastián & Muñoz, Juan Carlos & de Grange, Louis, 2011. "A topological route choice model for metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 138-147, February.
    2. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
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    Cited by:

    1. Zhou, Yuyang & Zheng, Shuyan & Hu, Zhonghui & Chen, Yanyan, 2022. "Metro station risk classification based on smart card data: A case study in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).

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