IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v594y2022ics0378437122000929.html
   My bibliography  Save this article

Metro station risk classification based on smart card data: A case study in Beijing

Author

Listed:
  • Zhou, Yuyang
  • Zheng, Shuyan
  • Hu, Zhonghui
  • Chen, Yanyan

Abstract

As the hub of urban railway transit, metro stations portray the skeleton structure of the public transit network. This study proposes a method of station classification from the dual perspectives of network structure and passenger flow. Each perspective considers the two aspects, one is the characteristics of the node itself, such as degree and the entrance and exit ridership; another considers the characteristics of the influence of other nodes, such as betweenness centrality and passing flow. Among them, the importance index of passing flow is calculated by the PageRank algorithm. According to these characteristics, metro stations are classified by k-means clustering algorithm after dimensionality reduction. The case study is conducted through nearly five million records from 278 stations in Beijing. From the classification results, stations are divided into six categories. Qualitative and quantitative regulations are proposed to reduce the risk of high ridership stations and improve the operation efficiency for few ridership stations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:594:y:2022:i:c:s0378437122000929
    DOI: 10.1016/j.physa.2022.127019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122000929
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    2. 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.
    3. von Ferber, C. & Holovatch, T. & Holovatch, Yu. & Palchykov, V., 2007. "Network harness: Metropolis public transport," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 585-591.
    4. Sui, Yi & Shao, Fengjing & Yu, Xiang & Sun, Rencheng & Li, Shujing, 2019. "Public transport network model based on layer operations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 984-995.
    5. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    6. Yang, Yuanzhi & Yu, Lei & Wang, Xing & Zhou, Zhongliang & Chen, You & Kou, Tian, 2019. "A novel method to evaluate node importance in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    7. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    8. Feng, Jia & Li, Xiamiao & Mao, Baohua & Xu, Qi & Bai, Yun, 2017. "Weighted complex network analysis of the Beijing subway system: Train and passenger flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 213-223.
    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. Yu, Liping & Liu, Huiran & Fang, Zhiming & Ye, Rui & Huang, Zhongyi & You, Yayun, 2023. "A new approach on passenger flow assignment with multi-connected agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    2. Yang, Xingxing & Li, Yang & Guo, Xin & Ding, Meiling & Yang, Jingxuan, 2023. "Simulation of energy-efficient operation for metro trains: A discrete event-driven method based on multi-agent theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    3. Meng, Yangyang & Zhao, Xiaofei & Liu, Jianzhong & Qi, Qingjie & Zhou, Wei, 2023. "Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    4. Yuchen Zhou & Yuhong Tian & Chi Yung Jim & Xu Liu & Jingya Luan & Mengxuan Yan, 2022. "Effects of Public Transport Accessibility and Property Attributes on Housing Prices in Polycentric Beijing," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    5. Hongxia Feng & Yaotong Chen & Jinyi Wu & Zhenqian Zhao & Yuanqing Wang & Zhuoting Wang, 2023. "Urban Rail Transit Station Type Identification Based on “Passenger Flow—Land Use—Job-Housing”," Sustainability, MDPI, vol. 15(20), pages 1-24, October.

    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. Xueguo Xu & Chen Xu & Wenxin Zhang, 2022. "Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability," Sustainability, MDPI, vol. 14(12), pages 1-26, June.
    2. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    3. Dong, Chen & Xu, Guiqiong & Meng, Lei & Yang, Pingle, 2022. "CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. Meng, Yangyang & Zhao, Xiaofei & Liu, Jianzhong & Qi, Qingjie & Zhou, Wei, 2023. "Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    5. Yangyang Meng & Qingjie Qi & Jianzhong Liu & Wei Zhou, 2022. "Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    6. Wang, Ning & Gao, Ying & He, Jia-tao & Yang, Jun, 2022. "Robustness evaluation of the air cargo network considering node importance and attack cost," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Luo, Ding & Cats, Oded & van Lint, Hans & Currie, Graham, 2019. "Integrating network science and public transport accessibility analysis for comparative assessment," Journal of Transport Geography, Elsevier, vol. 80(C).
    8. Sui, Yi & Shao, Fengjing & Yu, Xiang & Sun, Rencheng & Li, Shujing, 2019. "Public transport network model based on layer operations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 984-995.
    9. Huili Zhang & Yinfeng Xu & Xingang Wen, 2015. "Optimal shortest path set problem in undirected graphs," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 511-530, April.
    10. Hu, Baoyu & Feng, Shumin & Li, Jinyang & Zhao, Hu, 2018. "Statistical analysis of passenger-crowding in bus transport network of Harbin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 426-438.
    11. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.
    12. Yangyang Meng & Xiaofei Zhao & Jianzhong Liu & Qingjie Qi, 2023. "Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    13. Chen Zhang & Yichen Liang & Tian Tian & Peng Peng, 2024. "Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    14. Cicchini, Tomás & Caridi, Inés & Ermann, Leonardo, 2024. "Robustness of the public transport network against attacks on its routes," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    15. Melchiori, Anna & Sgalambro, Antonino, 2020. "A branch and price algorithm to solve the Quickest Multicommodity k-splittable Flow Problem," European Journal of Operational Research, Elsevier, vol. 282(3), pages 846-857.
    16. Hu, Baoyu & Feng, Shumin & Nie, Cen, 2017. "Bus transport network of Shenyang considering competitive and cooperative relationship," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 259-268.
    17. Luss, Hanan & Wong, Richard T., 2005. "Graceful reassignment of excessively long communications paths in networks," European Journal of Operational Research, Elsevier, vol. 160(2), pages 395-415, January.
    18. Rinaldi, Marco & Viti, Francesco, 2017. "Exact and approximate route set generation for resilient partial observability in sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 86-119.
    19. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Adaptive Routing and Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(4), pages 1326-1348, November.
    20. Chen, Bi Yu & Chen, Xiao-Wei & Chen, Hui-Ping & Lam, William H.K., 2020. "Efficient algorithm for finding k shortest paths based on re-optimization technique," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(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:eee:phsmap:v:594:y:2022:i:c:s0378437122000929. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.