面向多灾害中断的城市轨道交通网络韧性的拓扑决定因素
Topological Determinants of Resilience in Urban Rail Networks Facing Multi-Hazard Disruptions
AI总结：
本研究通过拓扑参数定量分析9个城市轨道交通网络的多灾害韧性，发现Domirank中心性下韧性更优，大网络脆弱但恢复更快，明确关键节点与网络结构对韧性的影响，为轨交规划提供参考。
摘要：
本研究针对9个主要城市轨道交通网络——华盛顿特区、波士顿、芝加哥、德里、东京、巴黎、上海、伦敦和纽约——的多灾害场景，采用聚焦拓扑参数的定量方法评估网络韧性，考察了韧性的两大关键组成部分：失效与恢复。研究运用基于渗流的网络拆解方法，如节点顺序移除（Sequential Removal of Nodes）和最大连通分支（Giant Connected Component）分析，结合随机攻击、基于中心性的定向攻击以及洪水失效场景，结果表明Domirank中心性在中断和恢复阶段均展现出更优的韧性表现。通过应用Kendall tau系数，进一步阐明了网络属性与韧性之间的关系，凸显出规模更大的网络虽更脆弱，但因固有冗余性和连通性而恢复更快。平均度、路径长度等关键属性始终影响恢复效果，而聚类系数与恢复的正相关性则凸显了局部互连的优势。本分析强调了关键节点的核心作用，以及平衡网络设计对提升韧性的重要性，为未来城市轨道交通系统应对多灾害威胁的规划提供了见解。
英文摘要：
This study examines the failure and recovery, two key components of resilience of nine major urban rail networks - Washington DC, Boston, Chicago, Delhi, Tokyo, Paris, Shanghai, London, and New York - against multi-hazard scenarios utilizing a quantitative approach focused on topological parameters to evaluate network resilience. Employing percolation-based network dismantling approach like Sequential Removal of Nodes and Giant Connected Component analysis, alongside random, centrality-based targeted attacks and flooding failure, findings reveal Domirank centrality's superior resilience in disruption and recovery phases. Kendall's tau coefficient's application further elucidates the relationships between network properties and resilience, underscoring larger networks' vulnerability yet faster recovery due to inherent redundancy and connectivity. Key attributes like average degree and path length consistently influence recovery effectiveness, while the clustering coefficient's positive correlation with recovery highlights the benefits of local interconnectivity. This analysis emphasizes the critical role of select nodes and the importance of balancing network design for enhanced resilience, offering insights for future urban rail system planning against multi-hazard threats.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
