Empirical Analysis of Impacts of Post-Disaster Human Mobility Patterns on the Resilience of Transportation Networks: Fid-Bau Portal

Empirical Analysis of Impacts of Post-Disaster Human Mobility Patterns on the Resilience of Transportation Networks

Ilbeigi, Mohammad / Meimand, Mostafa Ebrahimi

Resilient transportation networks are a critical component of urban societies during and after a disaster, necessary for emergency services, rescue operations, and access to major population and activity centers. Previous studies observed that beside physical damages to transportation infrastructure, post-disaster unusual traffic patterns may lead to gridlock, congestion, and failures in transportation networks. However, little is known about analyzing the impacts of post-disaster unusual human mobility and traffic patterns on the performance of transportation networks. The objective of this study is to empirically and statistically examine two hypotheses: 1) post-disaster unusual traffic patterns perturb the topology of transportation networks; and 2) perturbed topological features of the transportation network affect the network performance. Historical records of taxi GPS traces in New York City were used to examine the two hypotheses on the New York City road network after Hurricane Sandy in 2012. The results of the statistical process control using the exponentially weighted moving average control chart show that post-disaster unusual traffic patterns perturbed the topology of the New York City transportation network and significantly shifted the network betweenness index from its usual variations. The outcomes of the Granger causality test confirm the second hypothesis and indicate that the deviated betweenness index resulted from the perturbed network topology is statistically associated with the network closeness index indicating that the perturbed topological features of the network affect its performance. The outcomes of this study will help decision makers empirically analyze impacts of post-disaster unusual traffic patterns on performance and resiliency of transportation networks.