Identification of Disruptions and Recoveries in Airline Networks

Abstract

Disruptions in the air transportation system often lead to and are caused by demand-capacity imbalances, resulting in flight delays and cancellations as byproducts of traffic management and system recovery actions. In order to better understand 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 when did a disruption (or recovery) begin, how long did it last for, and where the disruption (or recovery) was experienced in the network. Identifying performance measures pertaining to the duration, intensity, and type of disruption is straightforward for individual airports; this is significantly more challenging for a large, geographically disparate, and interconnected network of airports. To address this, we first formalize the notion of disruption-recovery trajectories (DRTs). We show that these DRTs capture information regarding both the magnitude and spatial impact of disruptions in airline networks. Using DRTs, we identify past disruptions and recovery characteristics for four major US airlines, and analyze airline-specific relationships between flight delays and cancellations.

Keywords: Disruption and recovery in networks, Aviation disruptions, Graph signal processing, Flight delays and cancellations