# Evaluation of Urban Transportation Resilience under Extreme Weather Events
###### Abstract
Transportation resilience was measured by calculating the weighted average number of reliable independent paths with all the other urban nodes in the networks. [PERSON] et al. [28] proposed a road network infrastructure resilience framework to evaluate the resilience of the road connecting Manhattan to the rest of New York City. The resilience of the road network was measured by the ratio of travel time before and after a disruption. [PERSON] et al. [29] developed a data-driven approach for evaluating the resilience of the European passenger transportation networks during EWEs. [PERSON] et al. [30] developed a resilience index based on vehicle distance and travel time to quantitatively evaluate the resilience of the transportation network in the bridge-damaged areas of the Tampa Bay region in Florida. [PERSON] [31] used the systematic quantitative approach of cumulative sum (CUSUM) statistical process control charts to statistically monitor the closeness centrality of a transportation network through time, in order to detect unusual patterns due to an extreme event. [PERSON] et al. [32] proposed a method to quantitatively evaluate the urban public transportation resilience based on the system's function curves. Bus service rates and online taxi rates were used to characterize system functions, and quantitatively evaluate the public transportation resilience during the Zhengzhou storm and flooding in 2021. Based on the quantitative literature review, it was found that a major challenge for quantifying transportation resilience is the lack of empirical observations in disasters [33]. Most quantitative studies only consider the temporal variance of the transportation network. Specifically, the developed evaluation methods could not capture the real-time performance and recovery process of the transportation network in different subareas of the city under EWEs. The spatial variation of transportation resilience under different EWEs may lead to different levels of impact in different subareas [34]. It is important to propose targeted improvement plans and emergency policies based on the resilience level of different subareas and to allocate limited resources more effectively to the relatively low resilient subareas [35; 36]. Recently, the use of technologies such as GPS tracking systems has provided researchers with real-time trajectory information in urban areas [37]. [PERSON] and [PERSON] [38]analyzed a dataset of nearly 700 million taxi trips in New York City and introduced paces (normalized travel times) between regions of the city as the key performance indicator to detect and measure the impact of unusual events on transportation, such as Hurricane Sandy in 2012. The effect of unusual events on transportation was only detected and measured in this study, and urban transportation resilience was not quantified. [PERSON] et al. [39] used taxi and bus trajectory data in Nanjing to quantify human mobility perturbation in urban areas during EWEs. However, this study only focused on evaluating the human mobility perturbation, and the perturbation varied over the time span of EWEs, with no consideration given to the spatial variation of the perturbation. It did not provide improvement measures from an emergency management perspective based on the magnitude of the perturbation. In this study, a performance indicator and quantitative method are proposed for evaluating the urban transportation performance under EWEs based on bus GPS trajectory datasets from Fuzhou, China. It measures transportation performance by calculating the deviation of the total displacement of all buses under a rainstorm. Based on the transportation performance curve, this study quantifies transportation resilience and analyzes it in different subareas of the city across temporal and spatial dimensions.