Data-driven analysis of resilience in airline networks

Allen Wong, Sijian Tan, Keshav Ram Chandramouleeswaran, Huy T. Tran

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number102068
JournalTransportation Research Part E: Logistics and Transportation Review
Volume143
DOIs
StatePublished - Nov 2020

Keywords

  • Air transportation
  • Anomaly detection
  • Data analytics
  • Network theory
  • Resilience

ASJC Scopus subject areas

  • Business and International Management
  • Civil and Structural Engineering
  • Transportation

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