Volume 42 Issue 3
Jun.  2024
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Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(3): 167-174
DENG Yongliang, GAO Yutong, ZHOU Qi, LI Kewei, GU Tiantian. An Operational Resilience Evaluation of Subway Station Based on Improved CRITIC-VIKOR Method[J]. Journal of Transport Information and Safety, 2024, 42(3): 167-174. doi: 10.3963/j.jssn.1674-4861.2024.03.018
Citation: DENG Yongliang, GAO Yutong, ZHOU Qi, LI Kewei, GU Tiantian. An Operational Resilience Evaluation of Subway Station Based on Improved CRITIC-VIKOR Method[J]. Journal of Transport Information and Safety, 2024, 42(3): 167-174. doi: 10.3963/j.jssn.1674-4861.2024.03.018
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An Operational Resilience Evaluation of Subway Station Based on Improved CRITIC-VIKOR Method

doi: 10.3963/j.jssn.1674-4861.2024.03.018
  • 1.

    Suzhou University of Science and Technology, School of Civil Engineering, Suzhou 215009, Jiangsu, China

  • 2.

    China University of Mining and Technology, School of Mechanics and Civil Engineering, Xuzhou 221116, Jiangsu, China

  • Received Date: 2023-12-26
    Available Online: 2024-10-21
    Abstract
  • The operational resilience of subway stations is characterized by its multidimensionality and complexity. To evaluate this resilience accurately and reliably, an improved A CRITIC-VIKOR based evaluation method is proposed. Based on resilience theory and the operational characteristics of subway stations, the concept of operational resilience for subway stations is defined, with a focus on its three core dimensions: pressure, state, and response. Key factors in four domains, including human, machine, environment, and management, are analyzed, leading to the development of 8 primary indexes. Through literature analysis, 26 secondary evaluation indexes are established. To assign more accurate weights to the indexes, the standard deviation coefficient method is applied, improving the CRITIC method through correlation coefficient analysis. By integrating the VIKOR method, a novel evaluation model for subway station operational resilience is proposed. An empirical analysis is conducted on Tongli station, Liuhong station, and Nanmen station of Suzhou subway Line 4. Data is collected using a Likert scale, and weights are calculated for the 26 evaluation indexes, as well as the group utility, individual regret, and decision-making index for each station. This allows for a quantitative ranking of the operational resilience of subway stations. Compared with superiority chart, analytic hierarchy process, and entropy method, the improved CRITIC method showed the smallest bias range, from 2% to 40%, with the cumulative bias of the 26 indexes being the smallest at 553%. The results indicate that the weights for pressure resilience, state resilience, and response resilience are 59.74%, 21.48%, and 18.78%, respectively. Among the three stations, Liuhong station demonstrates the highest operational resilience, followed by Tongli station and Nanmen station. The evaluation model provides theoretical foundation for more accurate evaluation of subway station operational resilience.

     

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