摘要
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.
作者
王淑良
陈辰
张建华
栾声扬
WANG Shuliang;CHEN Chen;ZHANG Jianhua;LUAN Shengyang(School of Electrical Engineering and Automation,Jiangsu Normal University,Xuzhou 221116,China)
出处
《复杂系统与复杂性科学》
CAS
CSCD
北大核心
2022年第4期47-54,共8页
Complex Systems and Complexity Science
基金
国家自然科学基金(61801197,61503166)。
关键词
公共交通系统
复杂网络
深度学习
韧性
public transportation system
complex network
deep learning
resilience
作者简介
第一作者:王淑良(1981-),山东临沂人,博士,教授,主要研究方向为网络鲁棒性;通信作者:张建华(1980-),山东临沂人,博士,副教授,主要研究方向为交通网络鲁棒性。
