Resilience measurement and enhancement of population mobility network in Beijing-Tianjin-Hebei urban agglomeration under extreme rainfall impact

Abstract

In the context of globalization and regional integration, population mobility has become a critical vector for inter-city connections, essential for the stability and prosperity of urban agglomerations. However, extreme rainfall can disrupt transportation network connectivity and inter-city population flow, potentially triggering chain reactions in social sectors, thereby threatening the sustainable development of urban regions. This study examines the resilience of population mobility networks in the Beijing-Tianjin-Hebei urban agglomeration using Unicom's Smart Footprint big data. Through complex network analysis methods, this study simulates a 100-year extreme rainfall scenario and develops a comprehensive resilience assessment model. The model incorporates perturbation factors specific to extreme rainfall conditions and lays the foundation for optimization and enhancement strategies. The study finds that the population mobility network of the Beijing-Tianjin-Hebei urban agglomeration would experience a relative efficiency decline of 26 % under extreme rainfall scenarios. Heavy precipitation causes a mobility decrease of 50 % or more across cities in the agglomeration, with declines exceeding 90 % in Tianjin and its surrounding areas. In contrast, Beijing maintains its central position in the network both before and after the heavy rainfall disturbance. Finally, in the analysis of network efficiency recovery, the prioritized restoration of Tianjin's urban nodes significantly enhances overall network efficiency. Due to its higher connectivity and superior flood control infrastructure, Beijing remains largely unaffected in the network before and after the heavy rainfall disturbance. These findings provide practical guidance for enhancing population mobility networks resilience in both the Beijing-Tianjin-Hebei urban agglomeration and similar urban regions facing extreme rainfall events.