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Comparison analysis on complex topological network models of urban rail transit: A case study of Shenzhen Metro in China

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  • Meng, Yangyang
  • Tian, Xiangliang
  • Li, Zhongwen
  • Zhou, Wei
  • Zhou, Zhijie
  • Zhong, Maohua

Abstract

To study the topological complexity of urban rail transit (URT) networks with the multi-line transfer stations from different perspectives, Shenzhen Metro (SZM) is taken as an example and Space L & Space P models are established in this study. Then, based on multiple evaluation parameters and key nodes ranking, the differences of network topological complexity in two models are deeply explored and compared quantitatively. Some meaningful results have been obtained: (i) The characteristics of scale-free networks in Space L and Space P are proved through the eigenvector centrality distribution and truncated power-law distribution of cumulative degree. Scale-free networks show both robustness against random faults and vulnerability against deliberate attacks. The daily safety management at 16.87% of hub stations in Space P and 17.47% of hub stations in Space L should be taken seriously by metro managers in case of emergency events. (ii) Since the WS small-world effect in Space P model is more evident than that in Space L model, the connections among stations and OD accessibility of passenger are enhanced in Space P network. (iii) The important and risk nodes are concentrated in Space L and are more decentralized in Space P. P model has the stronger overall anti-attack capability than L model, which is more beneficial to the resilience of network. This study can realize the deeper understanding of URT system with different models and it can provide theoretical support for complex network analysis of URT system.

Suggested Citation

  • Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Comparison analysis on complex topological network models of urban rail transit: A case study of Shenzhen Metro in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    • Handle: RePEc:eee:phsmap:v:559:y:2020:i:c:s0378437120305379
    DOI: 10.1016/j.physa.2020.125031
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    as
    1. 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.
    2. Wang, Jin-Fa & He, Xuan & Si, Shuai-Zong & Zhao, Hai & Zheng, Chunyang & Yu, Hao, 2019. "Using complex network theory for temporal locality in network traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 722-736.
    3. Anderson, Taylor M. & Dragićević, Suzana, 2018. "Network-agent based model for simulating the dynamic spatial network structure of complex ecological systems," Ecological Modelling, Elsevier, vol. 389(C), pages 19-32.
    4. Wu, Xingtang & Dong, Hairong & Tse, Chi Kong & Ho, Ivan W.H. & Lau, Francis C.M., 2018. "Analysis of metro network performance from a complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 553-563.
    5. Sun, Lishan & Huang, Yuchen & Chen, Yanyan & Yao, Liya, 2018. "Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 108(C), pages 12-24.
    6. Sohn, Insoo, 2019. "A robust complex network generation method based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 593-601.
    7. Chen, Chong & Zhou, Xuan & Li, Zhuo & He, Zhiheng & Li, Zhengtian & Lin, Xiangning, 2018. "Novel complex network model and its application in identifying critical components of power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 316-329.
    8. Hossain, Md. Murad & Alam, Sameer, 2017. "A complex network approach towards modeling and analysis of the Australian Airport Network," Journal of Air Transport Management, Elsevier, vol. 60(C), pages 1-9.
    9. 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.
    10. Zhang, Wenjun & Deng, Weibing & Li, Wei, 2018. "Statistical properties of links of network: A survey on the shipping lines of Worldwide Marine Transport Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 218-227.
    11. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    12. Sui, Yi & Shao, Feng-jing & Sun, Ren-cheng & Li, Shu-jing, 2012. "Space evolution model and empirical analysis of an urban public transport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(14), pages 3708-3717.
    13. Wang, Jiaoe & Mo, Huihui & Wang, Fahui & Jin, Fengjun, 2011. "Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach," Journal of Transport Geography, Elsevier, vol. 19(4), pages 712-721.
    Full references (including those not matched with items on IDEAS)

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    2. 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.
    3. 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).
    4. Feng, Xiao & He, Shiwei & Li, Guangye & Chi, Jushang, 2021. "Transfer network of high-speed rail and aviation: Structure and critical components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    5. Lei Pang & Yuxiao Jiang & Jingjing Wang & Ning Qiu & Xiang Xu & Lijian Ren & Xinyu Han, 2023. "Research of Metro Stations with Varying Patterns of Ridership and Their Relationship with Built Environment, on the Example of Tianjin, China," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    6. 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.
    7. 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.
    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).
    9. 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.
    10. 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.

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