Resilience analysis of China's expressway network based on community detection in the context of urban agglomerations

This study aims to investigate the resilience of China’s expressway network in supporting the development of urban agglomerations, utilizing advanced intelligent computing techniques. Building upon complex network theory, we construct a comprehensive model of the expressway network, applying community detection algorithms to identify the structural and functional communities within the network. To assess the resilience of the expressway network, we propose a set of new performance metrics, including the community matching degree between urban agglomerations and the network, inter-community connectivity, and the inter-community traffic efficiency index. These indicators allow us to quantitatively evaluate how well the expressway network can facilitate efficient transportation between and within urban agglomerations, ensuring sustainable economic development. In addition, we simulate targeted attacks on critical nodes and key road segments spanning across different communities to identify vulnerabilities. Our analysis reveals that several cities and corridors, such as Qinhuangdao, Alxa League, Pingliang, and key routes like Tangshan-Qinhuangdao and Chuzhou-Bengbu, are essential in maintaining both intra- and inter-agglomeration connectivity. Disruptions to these critical areas would have a substantial impact on the overall resilience of the network. The results of this study provide valuable insights for optimizing the layout of China’s expressway network, improving corridor capacities, and enhancing overall traffic efficiency and resilience in the context of urban agglomeration development. Furthermore, it highlights the role of intelligent computing methods in facilitating the assessment and optimization of transportation infrastructure resilience, offering a theoretical foundation for future policy-making and urban planning strategies aimed at enhancing national expressway network resilience.