IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12515-d930784.html
   My bibliography  Save this article

Research on Structural Toughness of Railway City Network in Yellow River Basin and Case Study of Zhengzhou 7–20 Rainstorm Disaster

Author

Listed:
  • Yajun Xiong

    (College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China)

  • Hui Tang

    (College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China
    School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China)

  • Xiaobo Tian

    (College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China)

Abstract

With the gradual networking of inter-city relations and the increase in acute impact and chronic stress, the measurement of the resilience of urban network structures is particularly prominent. Based on the construction of the urban network by passenger train trips in the Yellow River Basin, this paper analyzes and assesses the characteristics of the structural resilience of the urban network, and probes into the network resilience and urban response under the circumstances of node failure and line failure in Zhengzhou. The main conclusions are as follows: (1) The urban network in the Yellow River Basin was clearly hierarchical, with a significant spatial distribution of “low in the north and high in the south”, and the overall characteristics of “robustness” in small areas and “fragility” in large areas. The network connection forms were diversified and open. The network transmission efficiency was high, and the edge cities depended on the core cities with prominent characteristics, and the risk load of regional core cities rose. (2) The network structure was “robust” as it maintained high operational efficiency and connectivity under random attacks. Under deliberate attacks, the city network operated efficiently with a small increase in connectivity before the 60% threshold, and after the threshold, the overall network started to split into many sub-networks, and the network fragmentation gradually increased until the network collapsed. (3) Zhengzhou node failure and line failure states in the Yellow River Basin urban network were resilient, in the sense that when suffering important nodes and lines going down it could still maintain good network operation efficiency, and the core nodes in the impact of natural disasters could adapt to the destructive nature of the network through the urban network structure self-regulation.

Suggested Citation

  • Yajun Xiong & Hui Tang & Xiaobo Tian, 2022. "Research on Structural Toughness of Railway City Network in Yellow River Basin and Case Study of Zhengzhou 7–20 Rainstorm Disaster," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12515-:d:930784
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12515/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/19/12515/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christopher G. Burton, 2015. "A Validation of Metrics for Community Resilience to Natural Hazards and Disasters Using the Recovery from Hurricane Katrina as a Case Study," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(1), pages 67-86, January.
    2. Lordan, Oriol & Sallan, Jose M. & Escorihuela, Nuria & Gonzalez-Prieto, David, 2016. "Robustness of airline route networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 18-26.
    3. Suleiman Hassan Otuoze & Dexter V. L. Hunt & Ian Jefferson, 2021. "Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    4. 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).
    5. Jungyeol Hong & Reuben Tamakloe & Soobeom Lee & Dongjoo Park, 2019. "Exploring the Topological Characteristics of Complex Public Transportation Networks: Focus on Variations in Both Single and Integrated Systems in the Seoul Metropolitan Area," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    6. Jialu Shi & Xuan Wang & Fuyi Ci & Kai Liu, 2022. "Spatiotemporal Characteristics and Patterns of the COVID-19 Pandemic in China: An Empirical Study Based on 413 Cities or Regions," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    7. Chao Fang & Piao Dong & Yi-Ping Fang & Enrico Zio, 2020. "Vulnerability analysis of critical infrastructure under disruptions: An application to China Railway High-speed," Journal of Risk and Reliability, , vol. 234(2), pages 235-245, April.
    8. Dirk Helbing, 2013. "Globally networked risks and how to respond," Nature, Nature, vol. 497(7447), pages 51-59, May.
    9. Yi Ge & Wen Dou & Haibo Zhang, 2017. "A New Framework for Understanding Urban Social Vulnerability from a Network Perspective," Sustainability, MDPI, vol. 9(10), pages 1-16, September.
    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. 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.
    2. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    3. Sellevåg, Stig Rune, 2021. "Changes in inoperability for interdependent industry sectors in Norway from 2012 to 2017," International Journal of Critical Infrastructure Protection, Elsevier, vol. 32(C).
    4. Igor Linkov & Benjamin Trump & Greg Kiker, 2022. "Diversity and inclusiveness are necessary components of resilient international teams," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-5, December.
    5. Xiao‐Bing Hu & Hang Li & XiaoMei Guo & Pieter H. A. J. M. van Gelder & Peijun Shi, 2019. "Spatial Vulnerability of Network Systems under Spatially Local Hazards," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 162-179, January.
    6. Yi Ge & Guangfei Yang & Yi Chen & Wen Dou, 2019. "Examining Social Vulnerability and Inequality: A Joint Analysis through a Connectivity Lens in the Urban Agglomerations of China," Sustainability, MDPI, vol. 11(4), pages 1-19, February.
    7. Man Li & Tao Ye & Peijun Shi & Jian Fang, 2015. "Impacts of the global economic crisis and Tohoku earthquake on Sino–Japan trade: a comparative perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 541-556, January.
    8. Pei, Shunshun & Zhai, Changhai & Hu, Jie, 2024. "Surrogate model-assisted seismic resilience assessment of the interdependent transportation and healthcare system considering a two-stage recovery strategy," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    9. Laura M. Canevari‐Luzardo & Frans Berkhout & Mark Pelling, 2020. "A relational view of climate adaptation in the private sector: How do value chain interactions shape business perceptions of climate risk and adaptive behaviours?," Business Strategy and the Environment, Wiley Blackwell, vol. 29(2), pages 432-444, February.
    10. Niccolò Casnici & Pierpaolo Dondio & Roberto Casarin & Flaminio Squazzoni, 2015. "Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.
    11. Yoshiharu Maeno & Kenji Nishiguchi & Satoshi Morinaga & Hirokazu Matsushima, 2014. "Impact of credit default swaps on financial contagion," Papers 1411.1356, arXiv.org.
    12. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    13. Jing Liu & Huapu Lu & Mingyu Chen & Jianyu Wang & Ying Zhang, 2020. "Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    14. Laure Rousset & César Ducruet, 2020. "Disruptions in Spatial Networks: a Comparative Study of Major Shocks Affecting Ports and Shipping Patterns," Networks and Spatial Economics, Springer, vol. 20(2), pages 423-447, June.
    15. Zhou, Yaoming & Wang, Junwei & Huang, George Q., 2019. "Efficiency and robustness of weighted air transport networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 14-26.
    16. Lordan, Oriol & Sallan, Jose M., 2019. "Core and critical cities of global region airport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 724-733.
    17. Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    18. Yusuke Toyoda, 2021. "Survey paper: achievements and perspectives of community resilience approaches to societal systems," Asia-Pacific Journal of Regional Science, Springer, vol. 5(3), pages 705-756, October.
    19. Xuelei Meng & Yahui Wang & Limin Jia & Lei Li, 2020. "Reliability Optimization of a Railway Network," Sustainability, MDPI, vol. 12(23), pages 1-27, November.
    20. Otto, Christian & Willner, Sven Norman & Wenz, Leonie & Frieler, Katja & Levermann, Anders, 2017. "Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate," OSF Preprints 7yyhd, Center for Open Science.

    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:14:y:2022:i:19:p:12515-:d:930784. 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.