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Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data

Author

Listed:
  • Gang Xue

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Daqing Gong

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Jianhai Zhang

    (Beijing Jingtou Urban Utility Tunnel Investment Co, Ltd., Beijing 100101, China)

  • Peng Zhang

    (Beijing Jingtou Urban Utility Tunnel Investment Co, Ltd., Beijing 100101, China)

  • Qimin Tai

    (Beijing Jingtou Urban Utility Tunnel Investment Co, Ltd., Beijing 100101, China)

Abstract

Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system.

Suggested Citation

  • Gang Xue & Daqing Gong & Jianhai Zhang & Peng Zhang & Qimin Tai, 2020. "Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data," Energies, MDPI, vol. 13(10), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2670-:d:362860
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    References listed on IDEAS

    as
    1. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    2. Singhal, Abhishek & Kamga, Camille & Yazici, Anil, 2014. "Impact of weather on urban transit ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 379-391.
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    Cited by:

    1. Zhitao Li & Yuzhen Shang & Guanwei Zhao & Muzhuang Yang, 2022. "Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
    2. Liang Zou & Ke Cao & Lingxiang Zhu, 2023. "Research on Relative Threshold of Abnormal Travel in Subway Based on Bilateral Curve Fitting," Mathematics, MDPI, vol. 11(8), pages 1-12, April.

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