摘要：Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public.
