Predicting Reactionary Delays in a Hub-Spoke Network using Graph Attention Neural Networks

Reactionary delays are a critical challenge in airline operations, especially within hub-spoke networks, where disruptions at spoke airports propagate and amplify throughout the fleet. Accurate prediction of these delays is essential for effective network planning, as errors can lead to flight cancellations, missed connections, and curfew infringements. However, current state-of-the-art delay prediction models do not fully integrate all elements that cause reactionary delays and affect subsequent operations. This study aims to close this gap by using a Graph Attention Network (GAT) model to predict reactionary delay distributions within a fleet network and identify the most critical flights through the analysis of attention weights. Using operational data from Swiss International Air Lines’ shorthaul fleet, the GAT model integrates node-level features, such as flight-specific parameters, and edge-level features, including rotational dependencies and passenger connections, to capture the spatial-temporal dynamics of delay propagation. The GAT model achieved reliable predictive accuracy, particularly on medium-delay days, of a root mean squared error of 15.59 minutes and a mean absolute error of 10.50 minutes. The results further reveal that the model comprehends the ripple effects caused by rotation delays. Furthermore, its attention weights confirm its capability to identify critical flights and connections, enabling the airline to allocate resources more effectively.