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Analysis of Crowded Propagation on the Metro Network

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  • Cai Jia

    (School of Geography and Tourism, Anhui Normal University, Huajin Campus, South 189 Jiuhua Rd, Wuhu 241002, China
    Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241008, China)

  • Shuyan Zheng

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Hanqiang Qian

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Bingxin Cao

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Kaiting Zhang

    (Quantutong Location Network Co., Ltd., 4F, No. 2 Courtyard, Liangshuihe No. 1 Street, Beijing 100176, China)

Abstract

The crowd in a metro system can cause inconvenience and even safety problems to passengers. The study of crowded propagation in metro systems can identify where and when crowds occur, ensuring travel quality and safety. Based on this, a modified susceptible–infected-susceptible (SIS) crowded propagation model is proposed to estimate the risk probability of crowding (RPC) in the metro network. Each station’s real transport capacity is considered. Infection rate and the recovery rate are proposed considering the traffic difference between stations. Using the Beijing metro network as a case study, the spatial and temporal patterns of crowded propagation are analyzed, and the types of nodes suitable for regulation are further discussed. This proposed model can provide a reference for RPC identification and regulation and promote sustainable development of metro operations.

Suggested Citation

  • Cai Jia & Shuyan Zheng & Hanqiang Qian & Bingxin Cao & Kaiting Zhang, 2022. "Analysis of Crowded Propagation on the Metro Network," Sustainability, MDPI, vol. 14(16), pages 1-12, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9829-:d:883818
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    References listed on IDEAS

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    1. Wei Yu & Xiaofei Ye & Jun Chen & Xingchen Yan & Tao Wang, 2020. "Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    2. Lianbo Deng & Junhao Zeng & Hongda Mei, 2019. "Passenger Flow Pushing Assignment Method for an Urban Rail Network Based on Hierarchical Path and Line Decomposition," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    3. Zhiyuan Huang & Ruihua Xu & Wei (David) Fan & Feng Zhou & Wei Liu, 2019. "Service-Oriented Load Balancing Approach to Alleviating Peak-Hour Congestion in a Metro Network Based on Multi-Path Accessibility," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    4. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    5. Christopher D. Higgins & Matthias N. Sweet & Pavlos S. Kanaroglou, 2018. "All minutes are not equal: travel time and the effects of congestion on commute satisfaction in Canadian cities," Transportation, Springer, vol. 45(5), pages 1249-1268, September.
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