Data-driven analysis of resilience in airline networks

Abstract

Network theory has provided key insights into the overall resilience of air transportation systems. We expand upon these insights by using Mahalanobis distance to quantify delay abnormalities, complex network metrics for high-level insights, and a hybrid method that combines data-driven and network approaches. We apply these methods to public data and discuss trends in resilience among four US airlines. We find that our data-driven methods enable more detailed insights into airline resilience than traditional network methods. We also find that simultaneously considering all three approaches provides a more comprehensive understanding of resilience than the consideration of any one in isolation.