IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v610y2023ics037843712200961x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712200961X
    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.2022.128403?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. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. 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).
    16. 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).
    17. 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).
    18. 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).
    19. 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.
    20. 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).
    21. 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).
    22. 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.
    23. 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).
    24. 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.
    25. 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.
    26. 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).
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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).
    3. 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).
    4. 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.
    5. Qingjie Qi & Yangyang Meng & Xiaofei Zhao & Jianzhong Liu, 2022. "Resilience Assessment of an Urban Metro Complex Network: A Case Study of the Zhengzhou Metro," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    6. Teqi Dai & Tiantian Ding & Qingfang Liu & Bingxin Liu, 2022. "Node Centrality Comparison between Bus Line and Passenger Flow Networks in Beijing," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    7. Abdelaty, Hatem & Mohamed, Moataz & Ezzeldin, Mohamed & El-Dakhakhni, Wael, 2022. "Temporal robustness assessment framework for city-scale bus transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    8. 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).
    9. Zhang, Lin & Xu, Min & Wang, Shuaian, 2023. "Quantifying bus route service disruptions under interdependent cascading failures of a multimodal public transit system based on an improved coupled map lattice model," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    10. Hu, Xinlei & Huang, Jie & Shi, Feng, 2022. "A robustness assessment with passenger flow data of high-speed rail network in China," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    11. 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).
    12. Chen, Junlan & Pu, Ziyuan & Guo, Xiucheng & Cao, Jieyu & Zhang, Fang, 2023. "Multiperiod metro timetable optimization based on the complex network and dynamic travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    13. 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).
    14. 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.
    15. Xinyu Zhuang & Li Zhang & Jie Lu, 2022. "Past—Present—Future: Urban Spatial Succession and Transition of Rail Transit Station Zones in Japan," IJERPH, MDPI, vol. 19(20), pages 1-35, October.
    16. Wang, Ying & Zhao, Ou & Zhang, Limao, 2024. "Modeling urban rail transit system resilience under natural disasters: A two-layer network framework based on link flow," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    17. Jin, Kun & Wang, Wei & Li, Xinran & Hua, Xuedong & Qin, Shaoyang, 2022. "Exploring the robustness of public transportation system on augmented network: A case from Nanjing China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    18. 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).
    19. Hongyan Dui & Yuheng Yang & Yun-an Zhang & Yawen Zhu, 2022. "Recovery Analysis and Maintenance Priority of Metro Networks Based on Importance Measure," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    20. Chan, Ho-Yin & Chen, Anthony & Li, Guoyuan & Xu, Xiangdong & Lam, William, 2021. "Evaluating the value of new metro lines using route diversity measures: The case of Hong Kong's Mass Transit Railway system," Journal of Transport Geography, Elsevier, vol. 91(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:610:y:2023:i:c:s037843712200961x. 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.