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.

The European Air Traffic Management system is among the most complex systems in the world. Due to the dense nature of the European network, consequences of disruptions are often catastrophic. In particular, disruptions altering the expected flying time tend to pose great challenges to the arrival management of busy hubs. In response, EUROCONTROL released the Target Time Management (TTM) system, allowing airlines to issue Target Times of Arrival (TTA) even before depart. The TTM system helps hubs airports coordinate arrivals and departures. From the point of view of airlines, the advantage resides in being able to prioritize early arrivals of critical flights. Nevertheless, real-time prioritization is not trivial. Many studies have focused on this problem but with results limited to slot swapping in a tactical context. This is less effective compared to airlines having the ability to select a new slot at the pre-tactical level. This work covers this gap, allowing airlines to select the desired TTA even before departure. We use Deep Reinforcement Learning to create a dynamic arrival allocation model capable of prioritizing flights in terms of passenger connecting time, curfew performance, rotation delay, and fairness to other airlines. Additionally, the model is capable of adapting and react to the uncertainty in responses from the TTM. In the real-world, large anticipations in TTAs are often rejected. The model is tested with real data from SWISS International Airline. Results show an improvement of 5.9 minutes for critical passenger connection and 4.8 minutes for rotation delay versus a deterministic approach.