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Dynamic Modeling for Metro Passenger Flows on Congested Transfer Routes

Author

Listed:
  • Weiyan Mu

    (School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Xin Wang

    (School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Chunya Li

    (School of Mathematics, Physics, and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Shifeng Xiong

    (NCMIS, KLSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

With the rapid development of urbanization, the metro becomes more and more important for people’s travel in big cities. To quantitatively describe metro passenger flows on congested transfer routes, this paper introduces a dynamic model based on automated data from the automatic fare collection (AFC) and automatic vehicle location (AVL) systems. An expectation maximization (EM) algorithm is proposed to compute the maximum likelihood estimates of unknown parameters in our model. Our model can yield a systematic analysis of one-transfer passenger flows on both population and individual aspects. Important characteristics, including transfer time, boarding probabilities, walking time, passenger-to-train assignment probabilities, and total travel time, can be inferred using only the AFC and AVL data. We provide a case study on the Beijing metro. Detailed analysis results based on our model are given. We also present a cross-validation method to validate our model with real data.

Suggested Citation

  • Weiyan Mu & Xin Wang & Chunya Li & Shifeng Xiong, 2023. "Dynamic Modeling for Metro Passenger Flows on Congested Transfer Routes," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1427-:d:1098240
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    References listed on IDEAS

    as
    1. Yong-Sheng Zhang & En-Jian Yao, 2015. "Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-11, December.
    2. Zhu, Yiwen & Koutsopoulos, Haris N. & Wilson, Nigel H.M., 2017. "A probabilistic Passenger-to-Train Assignment Model based on automated data," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 522-542.
    3. Takahiko Kusakabe & Takamasa Iryo & Yasuo Asakura, 2010. "Estimation method for railway passengers’ train choice behavior with smart card transaction data," Transportation, Springer, vol. 37(5), pages 731-749, September.
    4. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
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