摘要
随着我国城市地铁发展,地铁系统在面对自然灾害、突发事件等扰动事件时所表现出的韧性是目前备受关注的问题。为评估和优化地铁网络韧性,首先,深入分析拓扑结构,提出一种综合考虑网络连通水平和冗余水平的网络性能函数,并构建网络韧性评价指标;然后,在中断场景下,研究以韧性最优为目标的车站修复调度问题,采用遗传算法求解模型,明确各施工队的施工方案;最后,以台北捷运为例进行实例研究,验证方法的有效性。结果表明:多车站失效后,网络连通水平和冗余水平均有所下降,网络性能随之降低;在工期等约束下,最佳修复策略模型能够合理地制定施工方案以最大限度地提高地铁网络韧性;条件允许情况下,适当延长工期有助于进一步地提升最佳网络韧性。案例研究为城市地铁线网规划及提高地铁网络韧性提供有益借鉴。
With the development of China’s urban metro,the resilience of the metro system facing natural disasters,emergencies and other disturbance events has been a matter of great concern.To evaluate and optimize the resilience of metro networks,this paper first analyzed the topology in-depth,proposed a network performance function that took the connectivity level and redundancy level into account comprehensively,and constructed a metric for network resilience.Then,in the interruption scenario,the station restoration scheduling problem aiming at resilience optimization was investigated,and a genetic algorithm was adopted to solve the model and specify the construction plan for construction teams.Finally,the Taipei Metro was used as a case study to test the effectiveness of the proposed method.The results show that after multiple station failures,the network performance decreases due to connectivity level and redundancy level.Under duration constraints,the optimal restoration strategy model can rationalize the construction plan to maximize metro network resilience.If the conditions are satisfied,extending the construction period will help further enhance the optimal network resilience.This case study provides a useful reference for urban metro network planning and improving the resilience of metro networks.
作者
徐玉萍
侯明超
XU Yuping;HOU Mingchao(School of Transportation Engineering,East China Jiaotong University,Nanchang 330013,Jiangxi,China)
出处
《铁道运输与经济》
北大核心
2024年第6期198-206,214,共10页
Railway Transport and Economy
基金
江西省社会科学规划项目(22YJ17)
江西省研究生创新专项资金项目(YC2022-s561)。
关键词
网络韧性
连通水平
冗余水平
韧性评估
修复策略
韧性优化
Network Resilience
Connectivity Level
Redundancy Level
Resilience Assessment
Restoration Strategy
Resilience Optimization
