The Impact of Climate Change on Urban Transportation Resilience to Compound Extreme Events

Tao Ji, Yanhong Yao, Yue Dou, Shejun Deng, Shijun Yu, Yunqiang Zhu and Huajun Liao
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Tao Ji: College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Yanhong Yao: College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Yue Dou: College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Shejun Deng: College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Shijun Yu: College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Yunqiang Zhu: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Huajun Liao: SuperMap Software Co., Ltd., Beijing 100015, China

Sustainability, 2022, vol. 14, issue 7, 1-16

Abstract: 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.

Keywords: transportation resilience; compound extreme weather events; climate change; spatiotemporal dynamic evolution; future trends (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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