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Weighted Complex Network Analysis of Shanghai Rail Transit System

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  • Yingying Xing
  • Jian Lu
  • Shendi Chen

Abstract

With increasing passenger flows and construction scale, Shanghai rail transit system (RTS) has entered a new era of networking operation. In addition, the structure and properties of the RTS network have great implications for urban traffic planning, design, and management. Thus, it is necessary to acquire their network properties and impacts. In this paper, the Shanghai RTS, as well as passenger flows, will be investigated by using complex network theory. Both the topological and dynamic properties of the RTS network are analyzed and the largest connected cluster is introduced to assess the reliability and robustness of the RTS network. Simulation results show that the distribution of nodes strength exhibits a power-law behavior and Shanghai RTS network shows a strong weighted rich-club effect. This study also indicates that the intentional attacks are more detrimental to the RTS network than to the random weighted network, but the random attacks can cause slightly more damage to the random weighted network than to the RTS network. Our results provide a richer view of complex weighted networks in real world and possibilities of risk analysis and policy decisions for the RTS operation department.

Suggested Citation

  • Yingying Xing & Jian Lu & Shendi Chen, 2016. "Weighted Complex Network Analysis of Shanghai Rail Transit System," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-8, August.
    • Handle: RePEc:hin:jnddns:1290138
    DOI: 10.1155/2016/1290138
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    Cited by:

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    3. Peng Wu & Yunfei Li & Chengbing Li, 2022. "Invulnerability of the Urban Agglomeration Integrated Passenger Transport Network under Emergency Events," IJERPH, MDPI, vol. 20(1), pages 1-16, December.
    4. Dong, Xuefan & Tang, Daisheng & Tang, Chengxiang, 2021. "Social network analysis in China’s hospital healthcare," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    5. Zhang, Yifan & Ng, S. Thomas, 2021. "Unveiling the rich-club phenomenon in urban mobility networks through the spatiotemporal characteristics of passenger flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    6. Lin, Yi & Zhang, Jianwei & Yang, Bo & Liu, Hong & Zhao, Liping, 2019. "An optimal routing strategy for transport networks with minimal transmission cost and high network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 551-561.
    7. Feng, Huifang & Bai, Fengshan & Xu, Youji, 2019. "Identification of critical roads in urban transportation network based on GPS trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    8. Xu, Chen & Xu, Xueguo, 2024. "A two-stage resilience promotion approach for urban rail transit networks based on topology enhancement and recovery optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).

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