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Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours

Author

Listed:
  • Meng, Yangyang
  • Zhao, Xiaofei
  • Liu, Jianzhong
  • Qi, Qingjie
  • Zhou, Wei

Abstract

In the context of spatio-temporal big data, the complexity of urban metro network is highlighted. For the safe operation and resilient management of urban rail transit networks, it is advantageous to correctly comprehend the complex topological dynamics characteristics of the weighted metro network based on the massive mobility of passenger flow. In this study, the weighted Shenzhen Metro networks (WSZMNs) in the morning and evening rush hours were modeled based on Space L model and spatio-temporal big data of cross-sectional passenger flow. Combined with six complex indicators, the topological complexity of WSZMNs in two periods was compared based on quantitative and geographical distributions. Based on the multi-attribute decision making method, the weighted comprehensive importance of all nodes in morning and evening rush hours was also quantitatively evaluated and geographically visualized. Results indicate that the WSZMN exhibited some geographical heterogeneity, and the complexity of WSZMN in the morning rush hours was more prominent than in the evening rush hours. Additionally, for the network’s critical stations, essential locations, and significant periods, there was often large-scale and massive mobility of passenger flow. The metro operation management department should strengthen the targeted passenger flow control to improve the safety and resilience of Shenzhen Metro network. The relevant research findings help us get a better understanding of the complexity of metro network system under the massive passenger flow mobility in the rush hours. This study can provide specific theoretical and practical references for the urban smart metro operation department to manage the massive mobility better.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:610:y:2023:i:c:s037843712200961x
    DOI: 10.1016/j.physa.2022.128403
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    References listed on IDEAS

    as
    1. Yin, Dezhi & Huang, Wencheng & Shuai, Bin & Liu, Hongyi & Zhang, Yue, 2022. "Structural characteristics analysis and cascading failure impact analysis of urban rail transit network: From the perspective of multi-layer network," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Zhang, Yanjie & Ayyub, Bilal M. & Saadat, Yalda & Zhang, Dongming & Huang, Hongwei, 2020. "A double-weighted vulnerability assessment model for metrorail transit networks and its application in Shanghai metro," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
    3. Chai, Naijie & Zhou, Wenliang & Hu, Xinlei, 2022. "Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China," Transport Policy, Elsevier, vol. 125(C), pages 267-288.
    4. Zhou, Yu & Yang, Hai & Wang, Yun & Yan, Xuedong, 2021. "Integrated line configuration and frequency determination with passenger path assignment in urban rail transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 134-151.
    5. Yang, Zhijie & Chen, Xiaolong, 2018. "Evolution assessment of Shanghai Urban Rail Transit Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1263-1274.
    6. Xu, Zizhen & Chopra, Shauhrat S., 2022. "Network-based Assessment of Metro Infrastructure with a Spatial–temporal Resilience Cycle Framework," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Soh, Harold & Lim, Sonja & Zhang, Tianyou & Fu, Xiuju & Lee, Gary Kee Khoon & Hung, Terence Gih Guang & Di, Pan & Prakasam, Silvester & Wong, Limsoon, 2010. "Weighted complex network analysis of travel routes on the Singapore public transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5852-5863.
    8. Feng, Shumin & Xin, Mengwei & Lv, Tianling & Hu, Baoyu, 2019. "A novel evolving model of urban rail transit networks based on the local-world theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    9. Zhang, Lin & Wen, Huiying & Lu, Jian & Lei, Da & Li, Shubin & Ukkusuri, Satish V., 2022. "Exploring cascading reliability of multi-modal public transit network based on complex networks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    10. Haonan Ye & Xiao Luo, 2021. "Cascading Failure Analysis on Shanghai Metro Networks: An Improved Coupled Map Lattices Model Based on Graph Attention Networks," IJERPH, MDPI, vol. 19(1), pages 1-17, December.
    11. 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.
    12. Yinghan Zhu & Liudan Jiao & Yu Zhang & Ya Wu & Xiaosen Huo, 2021. "Sustainable Development of Urban Metro System: Perspective of Coordination between Supply and Demand," IJERPH, MDPI, vol. 18(19), pages 1-24, September.
    13. Yuanyuan Guo & Linchuan Yang & Wenke Huang & Yi Guo, 2020. "Traffic Safety Perception, Attitude, and Feeder Mode Choice of Metro Commute: Evidence from Shenzhen," IJERPH, MDPI, vol. 17(24), pages 1-20, December.
    14. Ruan, Zhongyuan & Song, Congcong & Yang, Xu-hua & Shen, Guojiang & Liu, Zhi, 2019. "Empirical analysis of urban road traffic network: A case study in Hangzhou city, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    15. Shiping Wen & Jiangang Shi & Wei Zhang & Ali Minai, 2021. "Impact of Urban Rail Transit Network on Residential and Commercial Land Values in China: A Complex Network Perspective," Complexity, Hindawi, vol. 2021, pages 1-11, May.
    16. Nicanor García Álvarez & Belarmino Adenso-Díaz & Laura Calzada-Infante, 2021. "Maritime Traffic as a Complex Network: a Systematic Review," Networks and Spatial Economics, Springer, vol. 21(2), pages 387-417, June.
    17. 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).
    18. Liang, Cong & Huang, Yaoxuan & Yip, Tsz Leung & Li, Victor Jing, 2022. "Does rail transit development gentrify neighborhoods? Evidence from Hong Kong," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 354-372.
    19. Lu, Qing-Chang & Zhang, Lei & Xu, Peng-Cheng & Cui, Xin & Li, Jing, 2022. "Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    20. 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.
    21. Kopsidas, Athanasios & Kepaptsoglou, Konstantinos, 2022. "Identification of critical stations in a Metro System: A substitute complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    22. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    23. Ma, Min & Hu, Dawei & Chien, Steven I-Jy & Liu, Jie & Yang, Xing & Ma, Zhuanglin, 2022. "Evolution assessment of urban rail transit networks: A case study of Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    24. 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).
    25. Cats, Oded, 2017. "Topological evolution of a metropolitan rail transport network: The case of Stockholm," Journal of Transport Geography, Elsevier, vol. 62(C), pages 172-183.
    26. Jia Feng & Xiamiao Li & Baohua Mao & Qi Xu & Yun Bai, 2016. "Weighted Complex Network Analysis of the Different Patterns of Metro Traffic Flows on Weekday and Weekend," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-10, December.
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