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Evaluating the Dynamic Comprehensive Resilience of Urban Road Network: A Case Study of Rainstorm in Xi’an, China

Author

Listed:
  • Yilin Hong

    (Department of Traffic Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Zhan Zhang

    (School of Design, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Xinyi Fang

    (Department of Traffic Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Linjun Lu

    (Department of Traffic Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management.

Suggested Citation

  • Yilin Hong & Zhan Zhang & Xinyi Fang & Linjun Lu, 2024. "Evaluating the Dynamic Comprehensive Resilience of Urban Road Network: A Case Study of Rainstorm in Xi’an, China," Land, MDPI, vol. 13(11), pages 1-24, November.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1894-:d:1519316
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    References listed on IDEAS

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    1. Yang Wei & Tetsuo Kidokoro & Fumihiko Seta & Bo Shu, 2024. "Spatial-Temporal Assessment of Urban Resilience to Disasters: A Case Study in Chengdu, China," Land, MDPI, vol. 13(4), pages 1-24, April.
    2. R. Luce & Albert Perry, 1949. "A method of matrix analysis of group structure," Psychometrika, Springer;The Psychometric Society, vol. 14(2), pages 95-116, June.
    3. Jiayu Ding & Yuewei Wang & Chaoyue Li, 2024. "A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems," Land, MDPI, vol. 13(6), pages 1-27, May.
    4. 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.
    5. Taghizadeh, Mehdi & Mahsuli, Mojtaba & Poorzahedy, Hossain, 2023. "Probabilistic framework for evaluating the seismic resilience of transportation systems during emergency medical response," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
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