Generative Short-Term Aircraft Trajectory Prediction with Conditional Flow Matching

Abstract

Reliable short-term aircraft trajectory prediction is essential for safety and efficiency in Air Traffic Management (ATM). This work introduces a generative framework for probabilistic 4D trajectory forecasting based on Conditional Flow Matching (CFM), a recent deep generative modeling approach that combines stable likelihood-based training with efficient sampling. The model is trained on historical ADS–B data from the OpenSky Network to predict aircraft motion over a 60 s horizon, conditioned on the preceding 60 s of observations. The model generates ensembles of realistic future trajectories that capture the inherent uncertainty of aircraft motion and enable probabilistic assessment of potential conflicts. As an application, we estimate the probability of mid-air collision during a loss-of-separation event using Monte Carlo simulation over the generated trajectories, providing a quantitative risk measure. The results demonstrate that flow-based generative modeling offers a principled foundation for uncertainty-aware trajectory prediction and safety analysis in ATM.