IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v565y2021ics0378437120308761.html
   My bibliography  Save this article

A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China

Author

Listed:
  • Jia, Jianlin
  • Chen, Yanyan
  • Wang, Yang
  • Li, Tongfei
  • Li, Yongxing

Abstract

The rapid-developed COVID-19 has been defined as a global emergency by the World Health Organization. Meanwhile, various evidence indicates there is a positive correlation between the transmission and population density, especially in closed and semi-closed space. The urban rail transit, as one of the major mode choices for people to commute in big cities, carries thousands of passengers every day with relatively closed and limited space, which provides favorable conditions for the spread of the virus. If the surrounding area of any station was disrupted under COVID-19, not only the individual line but also the entire urban rail transit network will have the risk to be affected. Therefore, it is necessary to identify and explore the distribution law of key stations during the spreading process of the COVID-19 virus in the urban rail transit network during the COVID-19 pandemic. Based on the spatial distribution of epidemic area and the demand of urban rail transit passengers, we have proposed a construction method of the rail transit network and use the improved shortest path algorithm to determine the route diversity index of each station which indicates its importance in the urban rail transit network. On this basis, we identify the key stations of the Beijing rail transit network to ensure that passengers avoid high-risk stations during the epidemic. The results show that the number of reasonable routes between any two stations is 1 to 5 during the COVID-19 pandemic. Moreover, the routes diversity index of the Beijing rail transit network was 1.235 during the COVID-19 pandemic and 2.2574 in the normal period. According to the reasonable route diversity index, we have identified the key stations of the Beijing rail transit network during the COVID-19, such as Qi-Li-Zhuang station.

Suggested Citation

  • Jia, Jianlin & Chen, Yanyan & Wang, Yang & Li, Tongfei & Li, Yongxing, 2021. "A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s0378437120308761
    DOI: 10.1016/j.physa.2020.125578
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120308761
    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.2020.125578?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. Yen‐Liang Chen & Kwei Tang, 2005. "Finding the Kth shortest path in a time‐schedule network," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(1), pages 93-102, February.
    2. Zhang, Jianhua & Wang, Shuliang & Zhang, Zhaojun & Zou, Kuansheng & Shu, Zhan, 2016. "Characteristics on hub networks of urban rail transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 502-507.
    3. Liu, Yang & Li, Yuanyuan & Hu, Lu, 2018. "Departure time and route choices in bottleneck equilibrium under risk and ambiguity," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 774-793.
    4. Leurent, Fabien M., 1997. "Curbing the computational difficulty of the logit equilibrium assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 31(4), pages 315-326, August.
    5. Du, Yuxian & Gao, Cai & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A new method of identifying influential nodes in complex networks based on TOPSIS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 57-69.
    6. Berdica, Katja, 2002. "An introduction to road vulnerability: what has been done, is done and should be done," Transport Policy, Elsevier, vol. 9(2), pages 117-127, April.
    7. Wu, Jianjun & Qu, Yunchao & Sun, Huijun & Yin, Haodong & Yan, Xiaoyong & Zhao, Jiandong, 2019. "Data-driven model for passenger route choice in urban metro network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 787-798.
    8. Fei, Liguo & Deng, Yong, 2017. "A new method to identify influential nodes based on relative entropy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 257-267.
    9. Zhan, Xiu-Xiu & Liu, Chuang & Zhou, Ge & Zhang, Zi-Ke & Sun, Gui-Quan & Zhu, Jonathan J.H. & Jin, Zhen, 2018. "Coupling dynamics of epidemic spreading and information diffusion on complex networks," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 437-448.
    10. Sun, Yeran & Mburu, Lucy & Wang, Shaohua, 2016. "Analysis of community properties and node properties to understand the structure of the bus transport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 523-530.
    11. Nian, Fuzhong & Yao, Shuanglong, 2018. "The epidemic spreading on the multi-relationships network," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 866-873.
    12. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    13. Yanjie Ji & Xinwei Ma & Mingyuan Yang & Yuchuan Jin & Liangpeng Gao, 2018. "Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    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. Patni, Sagar & Srinivasan, Sivaramakrishnan & Suarez, Juan, 2023. "The impact of COVID-19 on route-level changes in transit demand an analysis of five transit agencies in Florida, USA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    2. Ma, Zhiao & Yang, Xin & Wu, Jianjun & Chen, Anthony & Wei, Yun & Gao, Ziyou, 2022. "Measuring the resilience of an urban rail transit network: A multi-dimensional evaluation model," Transport Policy, Elsevier, vol. 129(C), pages 38-50.

    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. Jing Liu & Huapu Lu & Mingyu Chen & Jianyu Wang & Ying Zhang, 2020. "Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    2. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    3. Xu, Xiangdong & Chen, Anthony & Xu, Guangming & Yang, Chao & Lam, William H.K., 2021. "Enhancing network resilience by adding redundancy to road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    4. Jansuwan, Sarawut & Chen, Anthony & Xu, Xiangdong, 2021. "Analysis of freight transportation network redundancy: An application to Utah’s bi-modal network for transporting coal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 154-171.
    5. Xu, Xiangdong & Chen, Anthony & Jansuwan, Sarawut & Yang, Chao & Ryu, Seungkyu, 2018. "Transportation network redundancy: Complementary measures and computational methods," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 68-85.
    6. Zhu, Jingjing & Xu, Xiangdong & Wang, Zijian, 2023. "Economic evaluation of redundancy design for transportation networks under disruptions: Framework and case study," Transport Policy, Elsevier, vol. 142(C), pages 70-83.
    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. Jing, Weiwei & Xu, Xiangdong & Pu, Yichao, 2020. "Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Zhang, Hui & Zhuge, Chengxiang & Yu, Xiaohua, 2018. "Identifying hub stations and important lines of bus networks: A case study in Xiamen, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 394-402.
    10. Wang, Zhiru & Niu, Fangyan & Yang, Lili & Su, Guofeng, 2020. "Modeling a subway network: A hot-point attraction-driven evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    11. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.
    12. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    13. Mengying Cui & David Levinson, 2018. "Accessibility analysis of risk severity," Transportation, Springer, vol. 45(4), pages 1029-1050, July.
    14. Yin, Fulian & Jiang, Xinyi & Qian, Xiqing & Xia, Xinyu & Pan, Yanyan & Wu, Jianhong, 2022. "Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    15. Xinyuan Chen & Ruyang Yin & Qinhe An & Yuan Zhang, 2021. "Modeling a Distance-Based Preferential Fare Scheme for Park-and-Ride Services in a Multimodal Transport Network," Sustainability, MDPI, vol. 13(5), pages 1-14, March.
    16. Jenelius, Erik, 2010. "User inequity implications of road network vulnerability," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 2(3), pages 57-73.
    17. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    18. Mohamad Darayi & Kash Barker & Joost R. Santos, 2017. "Component Importance Measures for Multi-Industry Vulnerability of a Freight Transportation Network," Networks and Spatial Economics, Springer, vol. 17(4), pages 1111-1136, December.
    19. Jenelius, Erik & Mattsson, Lars-Göran, 2012. "Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 746-760.
    20. Maher, Mike, 1998. "Algorithms for logit-based stochastic user equilibrium assignment," Transportation Research Part B: Methodological, Elsevier, vol. 32(8), pages 539-549, November.

    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:565:y:2021:i:c:s0378437120308761. 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.