IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v638y2024ics037843712400147x.html
   My bibliography  Save this article

Resilience analysis of highway network under rainfall using a data-driven percolation theory-based method

Author

Listed:
  • Li, Yang
  • Wu, Jialu
  • Xiao, Yunjiang
  • Hu, Hangqi
  • Wang, Wei
  • Chen, Jun

Abstract

This paper proposes a data-driven approach using percolation theory to analyze the resilience of highway networks under rainfall conditions. The proposed approach's main contribution is integrating real-world traffic data with percolation theory to evaluate the impact of rainfall on traffic flow and identify the critical links of highway networks. The resilience indicators, accounting for network topology and functionality, were formulated. To calculate these indicators under various rainfall intensities, the traffic flow fundamental diagrams were established using empirical rainfall and traffic data, and a probabilistic rainfall simulation model was developed. A case study of the East Midlands, UK highway network under a heavy rainfall event on September 27, 2019, validated the approach's feasibility. Furthermore, control experiments showed that the critical links identified by the proposed method enhance highway network resilience more effectively than traditional methods, thus validated the novelty of our approach.

Suggested Citation

  • Li, Yang & Wu, Jialu & Xiao, Yunjiang & Hu, Hangqi & Wang, Wei & Chen, Jun, 2024. "Resilience analysis of highway network under rainfall using a data-driven percolation theory-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    • Handle: RePEc:eee:phsmap:v:638:y:2024:i:c:s037843712400147x
    DOI: 10.1016/j.physa.2024.129639
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712400147X
    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.2024.129639?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. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    2. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    3. Chang, Kuo-Hao & Sheu, Jiuh-Biing & Chen, Yenming J. & Chang, Chieh-Hsin & Liu, Chih-Hao, 2023. "Practice-based post-disaster road network connectivity analysis using a data-driven percolation theory-based method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    4. Verma, Muskan & Sharma, Sapna, 2023. "Modeling heterogeneity in an open percolation backbone fractal traffic network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    5. Tsai-Yun Liao & Ta-Yin Hu & Yi-No Ko, 2018. "A resilience optimization model for transportation networks under disasters," 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. 93(1), pages 469-489, August.
    6. Zhou, Yaoming & Wang, Junwei & Sheu, Jiuh-Biing, 2019. "On connectivity of post-earthquake road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 1-16.
    7. G. F. Newell, 1961. "Nonlinear Effects in the Dynamics of Car Following," Operations Research, INFORMS, vol. 9(2), pages 209-229, April.
    8. Chen, Jie & Tan, Xuegang & Cao, Jinde & Li, Ming, 2022. "Effect of coupling structure on traffic-driven epidemic spreading in interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    9. Guo, Shengmin & Wu, Ruoqian & Tong, Qingfeng & Zeng, Guanwen & Yang, Jian & Chen, Long & Zhu, Tongyu & Lv, Weifeng & Li, Daqing, 2018. "Is city traffic damaged by torrential rain?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1073-1080.
    10. Hooyberghs, H. & Van Schaeybroeck, B. & Indekeu, J.O., 2010. "Percolation on bipartite scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(15), pages 2920-2929.
    11. Katori, Machiko & Katori, Makoto, 2021. "Continuum percolation and stochastic epidemic models on Poisson and Ginibre point processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    12. Homayoun Hamedmoghadam & Mahdi Jalili & Hai L. Vu & Lewi Stone, 2021. "Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    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. Zou, Qiling & Chen, Suren, 2019. "Enhancing resilience of interdependent traffic-electric power system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Zhaoming Yang & Qi Xiang & Yuxuan He & Shiliang Peng & Michael Havbro Faber & Enrico Zio & Lili Zuo & Huai Su & Jinjun Zhang, 2023. "Resilience of Natural Gas Pipeline System: A Review and Outlook," Energies, MDPI, vol. 16(17), pages 1-19, August.
    3. Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    4. Gonçalves, L.A.P.J. & Ribeiro, P.J.G., 2020. "Resilience of urban transportation systems. Concept, characteristics, and methods," Journal of Transport Geography, Elsevier, vol. 85(C).
    5. Hosseini, Yaser & Mohammadi, Reza Karami & Yang, Tony Y., 2024. "A comprehensive approach in post-earthquake blockage prediction of urban road network and emergency resilience optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    6. Pan, Shouzheng & Yan, Hai & He, Jia & He, Zhengbing, 2021. "Vulnerability and resilience of transportation systems: A recent literature review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    7. 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).
    8. Mohammad Zaher Serdar & Sami G. Al-Ghamdi, 2021. "Resiliency Assessment of Road Networks during Mega Sport Events: The Case of FIFA World Cup Qatar 2022," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    9. Meng Wei & Jiangang Xu & Yiwen Wang, 2022. "Resilience Assessment of Traffic Networks in Coastal Cities under Climate Change: A Case Study of One City with Unique Land Use Characteristics," Land, MDPI, vol. 11(10), pages 1-21, October.
    10. Nicholas B. Rajkovich & Yasmein Okour, 2019. "Climate Change Resilience Strategies for the Building Sector: Examining Existing Domains of Resilience Utilized by Design Professionals," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    11. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2018. "An extended car-following model under V2V communication environment and its delayed-feedback control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 349-358.
    12. Bai, Lu & Wong, S.C. & Xu, Pengpeng & Chow, Andy H.F. & Lam, William H.K., 2021. "Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 524-539.
    13. Trucco, Paolo & Petrenj, Boris, 2023. "Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    14. Yunze Wang & Ranran Xu & Ke Zhang, 2022. "A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    15. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    16. Qiao, Yanfeng & Xue, Yu & Cen, Bingling & Zhang, Kun & Chen, Dong & Pan, Wei, 2024. "Study on particulate emission in two-lane mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    17. Tang, Qianyong & Li, Huajiao & Qi, Yajie & Li, Yang & Liu, Haiping & Wang, Xingxing, 2023. "The reliability of the trade dependence network in the tungsten industry chain based on percolation," Resources Policy, Elsevier, vol. 82(C).
    18. Bucar, Raif C.B. & Hayeri, Yeganeh M., 2020. "Quantitative assessment of the impacts of disruptive precipitation on surface transportation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    19. Chen, Chao & Yang, Ming & Reniers, Genserik, 2021. "A dynamic stochastic methodology for quantifying HAZMAT storage resilience," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    20. Milan Janić, 2018. "Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: the case of HSR (high speed rail)," Transportation, Springer, vol. 45(4), pages 1101-1137, July.

    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:638:y:2024:i:c:s037843712400147x. 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.