基于复杂网络的关联公共交通系统韧性分析

Resilience Analysis of Public Interdependent Transport System Based on Complex Network

为保证城市公共交通系统的正常运行,对其拓扑结构及韧性进行研究。依据城市公交与地铁之间的耦合距离,建立相互依赖的公共交通网络模型,通过深度学习对耦合网络的拓扑属性进行识别;结合熵权法与最优理想解距法提出节点综合重要性指标,根据节点重要性对节点进行择优恢复,对比不同恢复策略下的网络韧性。以武汉市公共交通网络为例,验证了该方法的适用性和准确性。

The topological characteristics and resilience analysis of public transportation systems are of great significance in urban management to ensure its safe and sustainable operation.This paper constructs a bus-metro interdependent network model based on the passenger transfer relationship and uses deep learning to identify their network topology attributes.A comprehensive importance indicator of the nodes is established by entropy weight-technique for order preference by similarity to ideal solution(EWM-TOPSIS),and the resilience of the network under different recovery strategies are analyzed.In order to verify the applicability and accuracy of the method,this study takes Wuhan′s public transportation network as an example,which has practical guiding significance for the post-disaster recovery and operation management of the urban public transportation system.