| Urban rail transit system is the key infrastructure to ensure the efficient and orderly operation of the city.In recent years,with the rapid growth of urban population,the scale of urban rail transit system represented by metro has expanded rapidly and gradually entered the stage of network operation.Due to the complexity and strong coupling of the structure,the operational efficiency and security of the metro network will face major challenges when it is affected by major disturbances such as earthquakes,rainstorms and terrorist attacks.At present,reliability,vulnerability and other indicators are mainly used to evaluate the stability of metro network under disturbance events.Reliability index usually uses probability to measure the ability of metro network to complete the specified tasks,and vulnerability index focuses on describing the potential consequences of metro network disturbance events.However,reliability and vulnerability are single-dimensional indicators,which cannot effectively evaluate the multi-dimensional characteristics of metro network performance under major disturbance events.Based on this,this thesis uses resilience as the measurement index of metro network performance under major disturbance events,and focuses on the evaluation and optimization of metro network resilience to carry out systematic research and take Chengdu metro as an example for case analysis.The principal research contents of this thesis are the following:(1)The characteristic index analysis of metro complex network and the comprehensive importance evaluation of nodes based on entropy weight method are carried out.The topological model of metro network is constructed based on Space L method,and seven characteristic indexes are selected to establish the importance evaluation system of metro network nodes.The comprehensive importance of metro network nodes is evaluated based on entropy weight method,and the K-means algorithm is used to cluster the importance ranking results.This conclusion can be used as the basis for daily maintenance and network structure optimization of metro network,and also provides a potential alternative for subsequent resilience optimization in this thesis.(2)The metro network topology resilience evaluation model and the service resilience evaluation model considering the spatial and temporal distribution of passenger flow are constructed.The topological efficiency and the service efficiency considering the temporal and spatial distribution of passenger flow are used as the measurement indexes to evaluate the metro network resilience,respectively.The evaluation model of metro network topology resilience and the evaluation model of service resilience are constructed.In the case study,the influence of different performance indexes and different disturbance scenarios on resilience is analyzed.The results show that there are significant differences in the recovery curve of metro network performance and the recovery order of failure nodes when the service efficiency and topological efficiency are used as the indexes to evaluate the metro network performance respectively.The influence of deliberate attack on metro network performance is greater than that of random attack.(3)The optimal recovery strategy based on genetic algorithm under specific disturbance scenarios is proposed.Based on genetic algorithm,the optimal recovery scheme is determined in all feasible schemes composed of failure nodes,so as to maximize the service resilience of metro network.In a variety of disturbance cases,the difference of the implementation effect between the priority recovery based on comprehensive importance,random recovery and the optimal recovery based on genetic algorithm is analyzed and compared.The results show that there are differences between the results of the resilience of the metro network considering the non-equilibrium of the spatial distribution of passenger flow in a certain period of time and the results of the resilience of the metro network considering the non-equilibrium of the spatial and temporal distribution of passenger flow.It shows that in the actual research,only considering the passenger flow distribution at a single time point may misjudge the performance of the metro network and obtain the recovery scheme that deviates from the expected.It demonstrates the superiority of the optimal recovery strategy based on genetic algorithm;it shows that the passenger flow loss coefficient and the number of resource conditions will have a certain impact on the optimization results of metro network resilience. |
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