Statistical process control for analyzing resilience of transportation networks

Abstract

Resilient transportation networks constitute a vital component of urban societies. Even a temporary disruption in the network can paralyze an entire society. Extreme events, such as natural disasters, may have serious destructive impacts on the performance of transportation networks. Reliable performance of transportation systems during an emergency is also critical for a timely, efficient, and successful emergency management operation. Quantitatively analyzing resilience of transportation networks is a critical step toward developing reliable urban societies. Advanced technologies, namely GPS tracking systems, provide rich information about human mobility in urban areas and can be used to monitor traffic patterns. This study proposes a systematic quantitative approach, to develop transportation networks and quantify their topological features using taxi GPS traces. A process control is developed to statistically monitor the topological features of the transportation network through time. Such analysis can allow detection of unusual patterns due to an extreme event and to analyze resilience of the transportation network. This methodology is used to analyze resilience of the New York City transportation network against Hurricane Sandy in 2012. The outcomes of this study will help decision makers analyze resilience of transportation networks, measure and compare destructive impacts of extreme events, and identify vulnerabilities in transportation networks.