Transportation resilience through cascading adaptation: national-scale evidence from China

• Reveals a disconnect between direct economic dependency and actual vulnerability in transportation networks. • Documents cascading adaptation with peripheral cities absorbing displaced flows during hub disruption. • Demonstrates stronger resilience in polycentric regions than in hub-centric regions. Transportation networks are critical infrastructure systems whose resilience determines regional freight connectivity and supply chain stability during major disruptions. While existing research has focused primarily on cascading failures, this study documents and quantifies a complementary empirical pattern, i.e., cascading adaptation, whereby freight flows across a national highway network sequentially reorganized to maintain functionality when core nodes are disrupted. We examine this phenomenon through the 2022 Shanghai lockdown, analyzing how China’s highway transportation network responded to the prolonged shutdown of a major logistics hub. Using 701,468,432 highway toll records covering nationwide freight movements, we develop a multi-scalar analytical framework to assess city-level freight disruption, dependency relationships, and propagation effects through hierarchical network structures. Our findings reveal a fundamental disconnect between direct freight dependency on Shanghai and actual vulnerability, suggesting that systemic risks propagate through complex, higher-order network pathways. Rather than experiencing uniform cascading failure, the network exhibited sequential flow redistribution consistent with adaptive reorganization: secondary hubs like Hangzhou emerged as alternative regional centers, while peripheral cities absorbed displaced flows and exceeded baseline activity levels. The extent of this adaptive response varied by regional structure: polycentric urban agglomerations demonstrated faster recovery trajectories, while hub-centric regions experienced prolonged disruption. These results advance understanding of freight network resilience by providing large-scale descriptive evidence for flow redistribution dynamics following hub disruption, offering empirically grounded observations relevant to resilient transportation planning.