Research On Resilience Assessment And Optimal Recovery Strategy Of Urban Rail Transit Network

Urban rail transit is an important infrastructure that supports the normal operation of cities.In recent years,China’s urban rail transit has developed rapidly,and more and more cities have entered a new stage of large-scale networked operation.Complex network characteristics,high density,and complex operation organization modes,as well as ultra-strong passenger flow have become the new normal of urban rail transit operations.At the same time,the frequent occurrence of disturbance events such as equipment failures and natural disasters poses a significant threat to the operational safety and efficiency of the rail transit network.Resilience emphasizes the entire process of resisting interference and restoring operational levels of rail transit networks under unexpected disturbances,providing new ideas for rail transit operation management.This paper synthesized multiple theories and methods such as complex networks,resilience,and optimization to construct a resilience evaluation model for urban rail transit networks and a network recovery timing optimization model,and proposed resilience improvement strategies.Firstly,it reviewed the relevant research background and current situation,introduced graph theory,complex networks,and resilience theories and methods,analyzed the formation mechanism of resilience based on the operational characteristics of urban rail transit networks,and laid a theoretical foundation for building resilience assessment models and recovery optimization models.Secondly,taking the Xi’an rail transit network as an example,a topology model was established to analyze the network topology characteristics and evolution rules.Aiming at problems such as existing evaluation methods that fail to fully consider the characteristics of the rail transit network,the node degree that considers the impact of neighbor nodes was used to represent the importance of the topology structure of the station,the passenger flow carrying capacity was used to reflect the difference in station functions,and the number of redundant paths between stations was used to reflect the level of network redundancy.Coupling the structural and functional characteristics of urban rail transit networks,a network performance evaluation model was constructed to conduct network performance analysis and identify important station.Then,an urban rail transit network resilience evaluation model was established to analyze the impact of recovery plans on network resilience based on the performance evaluation model.And a urban rail transit network recovery timing optimization model was constructed with the goal of maximizing network resilience and the constraints of recovery engineering resources and time,which was solved using adaptive genetic algorithms.Finally,based on the characteristics of urban rail transit emergencies,four types of disturbance scenarios were designed and solved using algorithms.By comparing them with empirical recovery strategies,the effectiveness of the proposed models and algorithms was proven,and the impact of recovery resource and time uncertainty on recovery plans was studied.The optimization results show that the optimal recovery strategy can achieve the best recovery effect in each scenario.The principle of priority recovery for failed sites is different under different disturbance scenarios.Recovery resources can improve recovery efficiency and resilience,but as resources increase,resilience growth slows down.Shorter recovery times increase site recovery priorities.