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Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro

Author

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  • Yanshuo Sun

    (University of Maryland)

  • Jungang Shi

    (East China Jiaotong University)

  • Paul M. Schonfeld

    (University of Maryland)

Abstract

This paper contributes to the emerging applications of automatically collected data in revealing the aggregate patterns of passenger flows and monitoring system performance from the passengers’ perspective. The paper’s main objectives are to (1) analyze passenger flow characteristics and (2) evaluate travel time reliability for the Shanghai Metro network by visualizing the automatic fare collection (AFC) data. First, key characteristics of passenger flows are identified by examining three major aspects, namely, spatial distribution of trips over the network, temporal distribution of passenger entries at the line level and station inflow/outflow imbalances. Second, travel time reliability analyses from the users’ perspective are performed, after a new metric of travel time reliability is designed. Comparisons of travel time reliability at the OD level are provided and the network reliabilities across multiple periods are also evaluated. Thus, this paper provides a comprehensive and holistic view of passenger travel experiences. Although the case study focuses on Shanghai Metro, the same analysis framework can be applied to other transit networks equipped with similar AFC systems.

Suggested Citation

  • Yanshuo Sun & Jungang Shi & Paul M. Schonfeld, 2016. "Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro," Public Transport, Springer, vol. 8(3), pages 341-363, December.
    • Handle: RePEc:spr:pubtra:v:8:y:2016:i:3:d:10.1007_s12469-016-0137-8
    DOI: 10.1007/s12469-016-0137-8
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    2. Gu, Yu & Fu, Xiao & Liu, Zhiyuan & Xu, Xiangdong & Chen, Anthony, 2020. "Performance of transportation network under perturbations: Reliability, vulnerability, and resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    3. Wu, Laiyun & Kang, Jee Eun & Chung, Younshik & Nikolaev, Alexander, 2021. "Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data," Omega, Elsevier, vol. 101(C).
    4. Paulsen, Mads & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2021. "Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 155-182.
    5. Jinjun Tang & Xiaolu Wang & Fang Zong & Zheng Hu, 2020. "Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    6. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    7. Jie Liu & Paul Schonfeld & Jinqu Chen & Yong Yin & Qiyuan Peng, 2021. "Perceived Trip Time Reliability and Its Cost in a Rail Transit Network," Sustainability, MDPI, vol. 13(13), pages 1-22, July.
    8. Wanxiang Wang & Ruijun Guo, 2022. "Travel Time Reliability of Highway Network under Multiple Failure Modes," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
    9. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    10. Li He & Martin Trépanier & Bruno Agard, 2021. "Space–time classification of public transit smart card users’ activity locations from smart card data," Public Transport, Springer, vol. 13(3), pages 579-595, October.
    11. Liu, Jie & He, Mingwei & Schonfeld, Paul M. & Kato, Hironori & Li, Anjun, 2022. "Measures of accessibility incorporating time reliability for an urban rail transit network: A case study in Wuhan, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 471-489.
    12. Pei Yin & Miaojuan Peng, 2023. "Station Layout Optimization and Route Selection of Urban Rail Transit Planning: A Case Study of Shanghai Pudong International Airport," Mathematics, MDPI, vol. 11(6), pages 1-29, March.
    13. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    14. Ting Chen & Jianxiao Ma & Shuang Li & Zhenjun Zhu & Xiucheng Guo, 2023. "Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit," Sustainability, MDPI, vol. 15(22), pages 1-17, November.
    15. Hainan Huang & Yi Lin & Jiancheng Weng & Jian Rong & Xiaoming Liu, 2018. "Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway," Sustainability, MDPI, vol. 10(12), pages 1-15, December.

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