Quantitative assessment measures resilience in railway transportation systems, suggesting improved disaster responses via Cloud Model integration.
Purpose This study aims to analyze the impact of disaster characteristics on the resilience of railway transportation. From multiple aspects, including the exposure degree, defense capacity, response capacity, adaptability and recovery capacity, the influencing factors of railway transportation resilience under natural disaster conditions are systematically expounded. Design/methodology/approach An evaluation system comprising 5 criterion layers, 23 secondary indicators and 54 tertiary indicators has been established. A quantitative assessment method integrating Cloud Model with Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation (AHP-FCE) is proposed for railway transportation resilience. The characteristic parameters of the cloud model (Expected value Ex, Entropy En, Hyper-entropy He) can accurately quantify the fuzziness and randomness of resilience indicators. Findings The cloud model is applied to conduct quantitative analysis and evaluation of multidimensional indicators, providing a quantitative analysis method for railway transportation emergency management. The research provides a quantitative analysis tool for resilience evaluation of railway transportation systems. Originality/value The results can be applied to the construction and operation of actual railway transportation systems, offering theoretical support for enhancing resistance capacity, response capacity, adaptability and recovery capacity, as well as improving railway transportation resilience.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: