Topological Determinants of Resilience in Urban Rail Networks Facing Multi-Hazard Disruptions (2407.06359v1)

Abstract: 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.