Understanding Urban Mobility Responses to Extreme Precipitation Events: A Case Study of Zhengzhou, China

Cities are increasingly vulnerable to the impacts of extreme weather events, understanding the travel responses to such events can support needs-based emergency resource allocation and long-term resilience planning. Although previous research has advanced our understanding of travel patterns during abnormal conditions, knowledge remains limited regarding how urban travel changes spatially (e.g. by different spatial clusters) during extreme precipitation events. To address this research gap, this study aims to assesses how urban travel, by number of trips, changed in response to extreme precipitation events using time series clustering and discrete choice modelling. The study uses cell phone signalling-based mobility big data before, during, and after the perturbations, with the 2021 Zhengzhou floods as a case study. Moreover, the aggregated responses of urban travel were empirically defined in terms of the magnitude of trip reduction and time-to-recovery, i.e. travel resilience. The study identifies four distinct groups that exhibit comparable response and recovery patterns, which can be subject to the influence of factors associated with the built environment. The study reveals that areas with higher road network density are comparatively more vulnerable to the initial impact of extreme rainfall and flooding. Nevertheless, taking a long-term perspective, higher road network density contributes to faster recovery and more robust travel resilience. Moreover, home-based work trips are less sensitive to red rainfall warnings than home-based other trips and non-home-based trips. This study provides valuable implications for planners and policymakers to resist similar future events.