Resilience assessment and optimal recovery strategy of rail transit network
WANG Song
LIU Jie
HUANG Jianchang
Abstract：To improve the recovery capability of rail transit networks in response to sudden disturbance events,a resilience assessment model for rail transit networks is proposed,which integrates traffic service efficiency and network operational efficiency.A recovery strategy model for rail transit networks is established with the objective of maximizing network resilience.By combining genetic algorithms with adaptive large neighborhood search algorithms,a hybrid adaptive large neighborhood search genetic algorithm is proposed to solve the resilience maximization recovery strategy model.Taking the rail transit system of Hangzhou as an example,disturbance scenarios such as natural disasters,human-made damages,and special control measures are simulated using random and deliberate attacks.The station recovery sequence and network resilience performance of random recovery strategies,node-degree-first recovery strategies,importance-first recovery strategies,and resilience-maximization recovery strategies are compared and analyzed under three disturbance scenarios.The results indicate that under all three disturbance scenarios,the resilience-maximization recovery strategy achieves the best repair effect on the rail transit network,followed by node-degree-first and importance-first recovery strategies,while the random recovery strategy exhibits the worst repair effect.In the human-made damage scenario,the rail transit network is most severely affected,and at this time,the resilience-maximization recovery strategy improves the network resilience by 6.7％,7.6％,and 25.1％compared to the other three strategies,respectively.Furthermore,when repairing one station,the rail transit network requires approximately 8 hours to recover to optimal performance,while repairing four stations simultaneously only requires about 3 hours to reach optimal recovery.The greater the number of stations that can be repaired simultaneously at the same time point,the faster the network resilience is restored.Implementing the optimal recovery strategy as soon as possible and increasing the repair resource investment in the early stage of the disturbance can significantly improve the recovery efficiency and network resilience of the rail transit network.
Keywords：rail transitnetwork resiliencerecovery strategygenetic algorithmadaptive large neighborhood search algorithm
Publication Date：2025-09-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 11-18 )