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Injury-Based Surrogate Resilience Measure: Assessing the Post-Crash Traffic Resilience of the Urban Roadway Tunnels

Author

Listed:
  • Chenming Jiang

    (China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
    Management School, Lancaster University, Lancaster LA1 4YW, UK)

  • Junliang He

    (China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
    Institutes of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Shengxue Zhu

    (Jiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huaian 223003, China)

  • Wenbo Zhang

    (The Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China)

  • Gen Li

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Weikun Xu

    (Jiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huaian 223003, China)

Abstract

Crash injuries not only result in huge property damages, physical distress, and loss of lives, but arouse a reduction in roadway capacity and delay the recovery progress of traffic to normality. To assess the resilience of post-crash tunnel traffic, two novel concepts, i.e., surrogate resilience measure (SRM) and injury-based resilience (IR), were proposed in this study. As a special kind of semi-closed infrastructure, urban tunnels are more vulnerable to traffic crashes and injuries than regular roadways. To assess the IR of the post-crash roadway tunnel traffic system, an over-one-year accident dataset comprising 8621 crashes in urban roadway tunnels in Shanghai, China was utilized. A total of 34 variables from 11 factors were selected to establish the IR assessment indicator system. Methodologically, to tackle the skewness issue in the dataset, a binary skewed logit (Scobit) model was found to be superior to a conventional logistic model and subsequently adopted for further analysis. The estimated results showed that 15 variables were identified to be significant in assessing the IR of the roadway tunnels in Shanghai. Finally, the formula for calculating the IR levels of post-crash traffic systems in tunnels was given and would be a helpful tool to mitigate potential trends in crash-related resilience deterioration. The findings of this study have implications for bridging the gap between conventional traffic safety research and system resilience modeling.

Suggested Citation

  • Chenming Jiang & Junliang He & Shengxue Zhu & Wenbo Zhang & Gen Li & Weikun Xu, 2023. "Injury-Based Surrogate Resilience Measure: Assessing the Post-Crash Traffic Resilience of the Urban Roadway Tunnels," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6615-:d:1122894
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    References listed on IDEAS

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    1. Shengdi Chen & Shiwen Zhang & Yingying Xing & Jian Lu, 2020. "Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel," IJERPH, MDPI, vol. 17(9), pages 1-15, May.
    2. Gu, Yu & Fu, Xiao & Liu, Zhiyuan & Xu, Xiangdong & Chen, Anthony, 2020. "Performance of transportation network under perturbations: Reliability, vulnerability, and resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    3. Pan, Xing & Dang, Yuheng & Wang, Huixiong & Hong, Dongpao & Li, Yuehong & Deng, Hongxu, 2022. "Resilience model and recovery strategy of transportation network based on travel OD-grid analysis," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Bai, Xiwen & Ma, Zhongjun & Zhou, Yaoming, 2023. "Data-driven static and dynamic resilience assessment of the global liner shipping network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
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