IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i15p6527-d1446342.html
   My bibliography  Save this article

Analysis of Topological Properties and Robustness of Urban Public Transport Networks

Author

Listed:
  • Yifeng Xiao

    (School of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Zhenghong Zhong

    (Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S10 2TN, UK)

  • Rencheng Sun

    (School of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

Abstract

With the acceleration of urbanization, public transport networks are an important part of urban transport systems, and their robustness is critical for city operation. The objective of this study is to analyze the topological properties and robustness of an urban public transport network (UPTN) with a view to enhancing the sustainability of urbanization. In order to present the topological structure of the UPTN, the L-Space complex network modeling method is used to construct a model. Topological characteristics of the network are calculated. Based on single evaluation indices of station significance, a comprehensive evaluation index is proposed as the basis for selecting critical stations. The UPTN cascading failure model is established. Using the proportion of the maximum connected subgraph as the evaluation index, the robustness of the UPTN is analyzed using different station significance indices and deliberate attack strategies. The public transport network of Xuzhou city is selected for instance analysis. The results show that the UPTN in Xuzhou city has small-world effects and scale-free characteristics. Although the network has poor connectivity, it is a convenient means to travel for residents with many independent communities. The network’s dynamic robustness is demonstrably inferior to its static robustness due to the prevalence of cascading failure phenomena. Specifically, the failure of important stations has a wider impact on the network performance. Improving their load capacity and distributing the routes via them will help bolster the network resistance against contingencies. This study provides a scientific basis and strategic recommendations for urban planners and public transport managers to achieve a more sustainable public transport system.

Suggested Citation

  • Yifeng Xiao & Zhenghong Zhong & Rencheng Sun, 2024. "Analysis of Topological Properties and Robustness of Urban Public Transport Networks," Sustainability, MDPI, vol. 16(15), pages 1-23, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6527-:d:1446342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/15/6527/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/15/6527/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    2. Sonnam Jo & Liang Gao & Feng Liu & Menghui Li & Zhesi Shen & Lida Xu & Zi-You Gao, 2021. "Cascading failure with preferential redistribution on bus–subway coupled network," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 32(08), pages 1-13, August.
    3. Hui Zhang & Peng Zhao & Jian Gao & Xiang-ming Yao, 2013. "The Analysis of the Properties of Bus Network Topology in Beijing Basing on Complex Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-6, March.
    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. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Vinayak, & Raghuvanshi, Adarsh & kshitij, Avinash, 2023. "Signatures of capacity development through research collaborations in artificial intelligence and machine learning," Journal of Informetrics, Elsevier, vol. 17(1).
    3. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Oct 2024.
    4. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    6. Wang, Xiaojie & Slamu, Wushour & Guo, Wenqiang & Wang, Sixiu & Ren, Yan, 2022. "A novel semi local measure of identifying influential nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    7. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    8. Ferreira, D.S.R. & Ribeiro, J. & Oliveira, P.S.L. & Pimenta, A.R. & Freitas, R.P. & Dutra, R.S. & Papa, A.R.R. & Mendes, J.F.F., 2022. "Spatiotemporal analysis of earthquake occurrence in synthetic and worldwide data," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    9. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    10. Qinghu Liao & Wenwen Dong & Boxin Zhao, 2023. "A New Strategy to Solve “the Tragedy of the Commons” in Sustainable Grassland Ecological Compensation: Experience from Inner Mongolia, China," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    11. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    12. Xueguo Xu & Chen Xu & Wenxin Zhang, 2022. "Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability," Sustainability, MDPI, vol. 14(12), pages 1-26, June.
    13. Jianhong Chen & Hongcai Ma & Shan Yang, 2023. "SEIOR Rumor Propagation Model Considering Hesitating Mechanism and Different Rumor-Refuting Ways in Complex Networks," Mathematics, MDPI, vol. 11(2), pages 1-22, January.
    14. Xiaokun Su & Chenrouyu Zheng & Yefei Yang & Yafei Yang & Wen Zhao & Yue Yu, 2022. "Spatial Structure and Development Patterns of Urban Traffic Flow Network in Less Developed Areas: A Sustainable Development Perspective," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
    15. Daniel Reisinger & Fabian Tschofenig & Raven Adam & Marie Lisa Kogler & Manfred Füllsack & Fabian Veider & Georg Jäger, 2024. "Patterns of stability in complex contagions," Journal of Computational Social Science, Springer, vol. 7(2), pages 1895-1911, October.
    16. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
    17. Vincenza Carchiolo & Marco Grassia & Michele Malgeri & Giuseppe Mangioni, 2022. "Co-Authorship Networks Analysis to Discover Collaboration Patterns among Italian Researchers," Future Internet, MDPI, vol. 14(6), pages 1-15, June.
    18. Gregory Gutin & Tomohiro Hirano & Sung-Ha Hwang & Philip R. Neary & Alexis Akira Toda, 2021. "The effect of social distancing on the reach of an epidemic in social networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 629-647, July.
    19. Jie, Ke-Wei & Liu, San-Yang & Sun, Xiao-Jun & Xu, Yun-Cheng, 2023. "A dynamic ripple-spreading algorithm for solving mean–variance of shortest path model in uncertain random networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    20. Anna Concas & Lothar Reichel & Giuseppe Rodriguez & Yunzi Zhang, 2021. "Iterative Methods for the Computation of the Perron Vector of Adjacency Matrices," Mathematics, MDPI, vol. 9(13), pages 1-16, June.

    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:gam:jsusta:v:16:y:2024:i:15:p:6527-:d:1446342. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.