DST ‐ GAT : Predicting Multi‐Flight Trajectories With Spatio‐Temporal Flight Correlation Dynamics Mining

Flight trajectory prediction is crucial for enabling the air traffic management system to anticipate future flight conflicts, allowing for the timely adoption of precautionary traffic control measures. To improve the prediction accuracy, some leading‐edge approaches leverage multi‐agent social neural networks or spatio‐temporal graph neural networks to mine the interactions among flights. However, these methods typically focus on learning the temporal states of individual flights driven by multi‐flight interactions, while underexploring the dynamic evolution of correlations among different or same flights at varying positions or times, resulting in suboptimal prediction performance. To address this gap, we propose a dynamic spatio‐temporal graph attention neural network (DST‐GAT) with an encoder‐decoder structure. DST‐GAT first draws on trajectory pattern mining to outline the multi‐flight interaction range, then respectively employs two types of spatio‐temporal graph edge encoders to capture the spatio‐temporal dynamics of multi‐flight interactions as well as the hyper‐temporal dynamics of individual flight flying trends. Extensive experiments on Beijing Terminal Manoeuvring Area operation data demonstrate that DST‐GAT outperforms state‐of‐the‐art methods in terms of prediction accuracy, highlighting its promising ability to exploit the intricate spatio‐temporal correlations among air traffic.