A data-driven method to assess the causes and impact of delay propagation in air transportation systems

Air transportation systems are exposed to disruptions, which have significant impact on operations. Airlines operate tight schedules to maximise resource utilisation, however, the lack of sufficient buffers often result in propagating delays. Thus, understanding how likely it is to experience delays, why they keep happening and what is their impact on airline operations are important steps for the management of the disruptions they cause. In this paper, we propose a data-driven method to empirically analyse how delays propagate and their impact on an airline schedule. Our multi-layer network method captures different variables that are influenced by schedule disruption, namely aircraft (tail), crew, passengers and their interfaces. The method is tested on the schedule disruptions of a hub-and-spoke airline where we empirically demonstrate that incorporating information in this multi-layered manner results in a more robust assessment of delay propagation. The method along with the empirical results of this study can support aviation system planners gain additional insights into flight delay propagation patterns and consequently support their resource allocation decisions while improving overall system performance.