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The objective of this study is to investigate the resilience of roads networks to extreme events using a GIS and network science approach. Using the specific case study of Chicago, three extreme event scenarios were simulated: (1) extreme flooding, (2) random zonal disturbance, and (3) central targeted disturbance. To measure their impacts and as a proxy for flows, we calculate and analyze how the betweenness centrality of each road segment is being redistributed in the network before and after each simulation. Moreover, by randomly selecting 100 nodes in the Chicago road system, we simulate 10,000 trips and examine how they are being affected by each extreme event scenario. Overall, we find that extreme events can have tremendous impacts. More importantly, different types of extreme events generate completely different impacts, and the notion of resilience therefore rapidly becomes sensitive to individual contexts, thus supporting the argument towards more scenario-based analyses.
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Kermanshah et al. (2014) studied this question.