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Vulnerability Assessment for Cascading Failure in the Highway Traffic System

Author

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  • Han Liu

    (School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73, Huanghe River Road, Nangang District, Harbin 150090, Heilongjiang, China)

  • Jian Wang

    (School of Management, Harbin Institute of Technology, No. 73, Huanghe River Road, Nangang District, Harbin 150090, Heilongjiang, China)

Abstract

Vulnerability assessment is of great significance to highway traffic system. As the widespread cascading failure may cause serious damage, factor identification is necessary to improve the security level of highway system. In this paper, the Dematel method is applied to identify the vulnerability priorities of important factors, and a new assessment method for highway system vulnerability is presented by considering system cascading failure. The traffic allocation and reallocation are then optimized under user equilibrium assignment model in different scenarios. Finally, the methodology is applied to the Heilongjiang highway network and the results shows that vulnerable sections in highway networks can be determined efficiently in our proposed process.

Suggested Citation

  • Han Liu & Jian Wang, 2018. "Vulnerability Assessment for Cascading Failure in the Highway Traffic System," Sustainability, MDPI, vol. 10(7), pages 1-12, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2333-:d:156425
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    References listed on IDEAS

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    1. Katja Berdica & Lars-Göran Mattsson, 2007. "Vulnerability: A Model-Based Case Study of the Road Network in Stockholm," Advances in Spatial Science, in: Alan T. Murray & Tony H. Grubesic (ed.), Critical Infrastructure, chapter 5, pages 81-106, Springer.
    2. Jenelius, Erik & Petersen, Tom & Mattsson, Lars-Göran, 2006. "Importance and exposure in road network vulnerability analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 537-560, August.
    3. Asadabadi, Ali & Miller-Hooks, Elise, 2017. "Assessing strategies for protecting transportation infrastructure from an uncertain climate future," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 27-41.
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    Cited by:

    1. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    2. Rahimi-Golkhandan, Armin & Aslani, Babak & Mohebbi, Shima, 2022. "Predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).

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