Identification and Prediction of Disruptions in Airline Networks

Abstract

Disruptions in the air transportation system oftentimes lead to demand-capacity imbalances, resulting in flight delays and cancellations as byproducts of traffic management and system recovery actions. In order to better predict the impact of disruptions, as well as provide more targeted and proactive system recovery actions, it is critical to unambiguously identify key characteristics such as: (1) When did the disruption occur, (2) what was the duration of the disruption and subsequent recovery, and (3) how will an ongoing disruption evolve. In particular, providing performance measures regarding the duration, intensity, and type of disruption is straightforward for individual airports; for a large, geographically disparate, and interconnected network of airports, these characteristics are less clear. To address this, we first formalize the notion of \textit{disruption-recovery trajectories} (DRTs) for the air transportation system through representing key network delay and cancellation performance measures as evolving between discrete states. These DRTs capture information regarding both the magnitude and spatial impact of air transportation system disruptions. Using this DRT framework, we report on past disruption and recovery characteristics for four major US airlines, as well as demonstrate the ability to predict short-term evolution of disruptions and recoveries without detailed schedule information, potentially enabling near-real time decision support for prescriptive management actions.