Volume 42 Issue 3
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ZHANG Jiefei, REN Gang, TANG Lei, DU Jianwei, GU Houyu, SONG Jianhua. A Review about Resilience Evaluation for Urban Multimodal Transportation Networks[J]. Journal of Transport Information and Safety, 2024, 42(3): 102-113. doi: 10.3963/j.jssn.1674-4861.2024.03.011
Citation: ZHANG Jiefei, REN Gang, TANG Lei, DU Jianwei, GU Houyu, SONG Jianhua. A Review about Resilience Evaluation for Urban Multimodal Transportation Networks[J]. Journal of Transport Information and Safety, 2024, 42(3): 102-113. doi: 10.3963/j.jssn.1674-4861.2024.03.011
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A Review about Resilience Evaluation for Urban Multimodal Transportation Networks

doi: 10.3963/j.jssn.1674-4861.2024.03.011
  • 1.

    School of Mining Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China

  • 2.

    School of Transportation, Southeast University, Nanjing 211189, China

  • Received Date: 2023-10-09
    Available Online: 2024-10-21
    Abstract
  • To improve the development of research about transportation resilience, this paper, focusing on urban multimodal transportation networks, summarizes the relevant studies on resilience evaluation in the literature. The definition and connotation of resilience are introduced. The indicators for resilience evaluation are summarized from the perspectives of network topology, supply-demand characteristics, and coupling relationships. The research of model-driven and data-driven resilience evaluation methods are introduced. The advantages and disadvantages of these methods are summarized as well. Fourth, measures to improve the resilience of transportation network are discussed from the perspectives of network design, emergency evacuation, and network restoration. The resilience optimization models and algorithms are summarized as well. The research deficiencies and future development directions are discussed. The results show that: ① the resilience evaluation of composite networks fails to fully consider the coupling characteristics. Besides, resilience evaluation is imprecise to depict variable traffic demand and travelers' travel behavior. ② The determination of indicator weights depend more on subjective judgement in model-driven resilience evaluation. Data-driven resilience evaluation focus on data analysis and result display, but lacks in-depth analysis of resilience evolution. ③ The optimization models targeting resilience improvement need to be improved in multi-objective decision making, computational efficiency in large-scale networks, and reproduction of real scenes. From these results, the suggestions for the future research are as follows: ① in the development of the network and the selection of indicators, the dependence of the composite network needs to be fully considered. Besides, and the coupling characteristics between the systems need to be scientifically reflected in evaluation models. ② It is suggested to cooperate with multiple departments to establish a complete and shared database, to explore the network resilience evaluation methods which are driven by both data and model, and to design high-efficient algorithms to support the rapid and accurate calculation of the resilience indicators. ③ The static discrete resilience evaluation should be developed into dynamic continuous resilience monitor, based on which the temporal-spatial evolution of network resilience and the evolution mechanism of traffic network resilience must be analyzed. ④ The refined network resilience decision optimization should be strengthen to reproduce the real event scenarios in data analysis and model development. Besides, it is necessary to further explore the application of AI algorithm to deal with the application of large-scale network optimization.

     

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