IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2670-d362860.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2670/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2670/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pan Wu & Jinlong Li & Yuzhuang Pian & Xiaochen Li & Zilin Huang & Lunhui Xu & Guilin Li & Ruonan Li, 2022. "How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method," Sustainability, MDPI, vol. 14(15), pages 1-31, August.
    2. 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.
    3. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    4. 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.
    5. An, Ran & Zahnow, Renee & Pojani, Dorina & Corcoran, Jonathan, 2019. "Weather and cycling in New York: The case of Citibike," Journal of Transport Geography, Elsevier, vol. 77(C), pages 97-112.
    6. Kandt, Jens & Leak, Alistair, 2019. "Examining inclusive mobility through smartcard data: What shall we make of senior citizens' declining bus patronage in the West Midlands?," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    7. Kashfi, Syeed Anta & Bunker, Jonathan M. & Yigitcanlar, Tan, 2016. "Modelling and analysing effects of complex seasonality and weather on an area's daily transit ridership rate," Journal of Transport Geography, Elsevier, vol. 54(C), pages 310-324.
    8. Marijo Vidas & Vladan Tubić & Ivan Ivanović & Marko Subotić, 2022. "One Approach to Quantifying Rainfall Impact on the Traffic Flow of a Specific Freeway Segment," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    9. Vergel-Tovar, C. Erik & Leape, Jonathan & Villegas Carrasquilla, Mónica & Peñas Arana, Maria Claudia & Toro Gonzalez, Daniel & Canon Rubiano, Leonardo & Salas Barón, Eliana & Martinez, Paulo, 2022. "Mapping the transit network of greater Cartagena with mobile phones: Coverage, accessibility, and informality," Journal of Transport Geography, Elsevier, vol. 105(C).
    10. Wei, Ming & Liu, Yan & Sigler, Thomas & Liu, Xiaoyang & Corcoran, Jonathan, 2019. "The influence of weather conditions on adult transit ridership in the sub-tropics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 106-118.
    11. Weng, JianCheng & Yu, JiangBo & Di, XiaoJian & Lin, PengFei & Wang, Jing-Jing & Mao, Li-Zeng, 2023. "How does the state of bus operations influence passengers’ service satisfaction? A method considering the differences in passenger preferences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    12. Miao, Qing & Welch, Eric W. & Sriraj, P.S., 2019. "Extreme weather, public transport ridership and moderating effect of bus stop shelters," Journal of Transport Geography, Elsevier, vol. 74(C), pages 125-133.
    13. Li, Junlong & Li, Xuhong & Chen, Dawei & Godding, Lucy, 2018. "Assessment of metro ridership fluctuation caused by weather conditions in Asian context: Using archived weather and ridership data in Nanjing," Journal of Transport Geography, Elsevier, vol. 66(C), pages 356-368.
    14. Yuxin He & Yang Zhao & Kwok Leung Tsui, 2021. "An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership," Transportation, Springer, vol. 48(3), pages 1185-1216, June.
    15. Yang, Xiaobao & Yue, Xianfei & Sun, Huijun & Gao, Ziyou & Wang, Wencheng, 2021. "Impact of weather on freeway origin-destination volume in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 143(C), pages 30-47.
    16. Sun, Daniel(Jian) & Ding, Xueqing, 2019. "Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 227-239.
    17. Wu, Pan & Xu, Lunhui & Zhong, Lingshu & Gao, Kun & Qu, Xiaobo & Pei, Mingyang, 2022. "Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations," Journal of Transport Geography, Elsevier, vol. 104(C).
    18. Tao, Sui & Corcoran, Jonathan & Hickman, Mark & Stimson, Robert, 2016. "The influence of weather on local geographical patterns of bus usage," Journal of Transport Geography, Elsevier, vol. 54(C), pages 66-80.
    19. Jiang, Shixiong & Cai, Canhuang, 2022. "Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China," Transport Policy, Elsevier, vol. 127(C), pages 158-170.
    20. Bean, Richard & Pojani, Dorina & Corcoran, Jonathan, 2021. "How does weather affect bikeshare use? A comparative analysis of forty cities across climate zones," Journal of Transport Geography, Elsevier, vol. 95(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2670-:d:362860. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.