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.

In the initial stage of emergency rescue for major railway emergencies, there may be insufficient emergency resources. In order to ensure that all the emergency demand points can be effectively and fairly rescued, considering the fuzzy properties of the parameters, such as the resource demand quantity, the dispatching time and the satisfaction degree, the railway emergency resources dispatching optimization model is studied, with multi- demand point, multi-depot, and multi-resource. Based on railway rescue features, it was proposed that the couple number of relief point - emergency point is the key to affect railway rescue cost and efficiency. Under the premise of the maximum satisfaction degree of quantity demanded at all emergency points, a multi-objective programming model is established by maximizing the satisfaction degree of dispatching time and the satisfaction degree of the couple number of relief point - emergency point. Combined with the ideal point method, a restrictive parameter interval method for optimal solution was designed, which can realize the quick seek of Pareto optimal solution. Furthermore, an example is given to verify the feasibility and effectiveness of the method.

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In view of the problems in the real-time traffic video monitoring that the adaptive vehicle extraction is greatly affected by the environmental factors such as the illumination, noise, and so on; the missed detection and error detection rate is high; and it is difficult to meet the robustness and the real-time performance at the same time, a kind of method for the adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm is put forward. In this method, based on the multi-objective particle swarm optimization algorithm, adaptive binarization processing is carried out on the image first, and the interference points are removed by filtration through the erosion and expansion method. Simple and effective method is used to carry out the merger of the shadow line and the extraction of the real-time traffic video. In the algorithm, the information entropy in the target area and the symmetry characteristics of the vehicle tail are used to screen and identify the region of interest, which has reduced the missed detection and error detection rate of the algorithm. The multi-objective particle swarm optimization algorithm is used to extract the vehicle boundaries and has achieved relatively good effect. The results show that the detection accuracy is 89% and the average operating speed is 17.6 frames/s during the processing of the real-time traffic video with the resolution of 640 × 480.

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