Topology shapes road network recovery: Global evidence from 224 cities

The ability of transport networks to recover from disruptions is critical for sustaining urban mobility and resilience. Yet, the extent to which the topology of road networks influences recovery outcomes remains poorly understood. Here, we combine large-scale simulation, network science, and statistical modelling to quantify how topological characteristics shape multidimensional resilience across 224 real-world cities worldwide. We develop a scalable stress-testing framework that simulates diverse disruption and recovery scenarios using open-source road data, evaluating recovery trajectories along four dimensions: operational efficiency, accessibility, spatial equity, and connectivity. The results reveal that road network resilience is inherently multidimensional: no single restoration strategy performs optimally across all objectives. Topological characteristics such as connectedness and degree heterogeneity strongly condition recovery trajectories, amplifying or constraining the effectiveness of restoration strategies depending on disruption scale. Graph-based heuristics, though comparable to random recovery under minor failures, accelerate recovery significantly when disruptions are large. These findings demonstrate that resilience emerges from the interactions between network design and recovery efforts, underscoring the need for policies that integrate robust, redundant topologies with flexible recovery planning. While developed in the context of transport networks, these findings are broadly applicable to other networked critical infrastructure systems, such as power, water, and telecommunications, whose functionality and recovery dynamics follow similar topological and operational principles.