Resilience assessment of urban mobility flow networks from different scales: A case study in shenzhen

Abstract

Urban mobility flow networks are vital for ensuring the functional efficiency of cities, supporting the movement of people, goods, and services. However, these networks are increasingly vulnerable to disruptions caused by factors such as extreme weather events, traffic accidents, and system failures. This study presents a multi-scale framework to assess the resilience of urban mobility flow networks, focusing on Shenzhen as a case study. By evaluating resilience at the macro, meso, and micro levels, the study investigates the impacts of disruptions and recovery processes across different spatial scales. Key findings reveal that a small subset of high-degree nodes and high-weight edges significantly influences network performance, with their removal causing rapid degradation and swift recovery upon restoration. The analysis also highlights that centrality metrics such as degree, betweenness, and eigenvector centrality are informative for assessing the resilience of urban mobility systems. At the macro scale, degree centrality nodes and weight-based edges exhibit the fastest failure and recovery dynamics, while eigenvector centrality ensures more stable long-term recovery. The meso and micro-scale analyzes underscore the importance of local connectivity and suggest that central districts exhibit stronger resilience compared to peripheral areas. The proposed method assesses urban mobility flow network resilience at multiple scales.