High-fidelity Aircraft Trajectory Generation Using a Flow-based Generative Model

Aircraft trajectory generation is one of the key technologies for realizing and researching a highly efficient air traffic system. Fast-time simulations are used for the initial evaluation of newly proposed operational concepts, and the employed scenarios must be faithful to actual aircraft operations and include a wide range of possible future events. In addition, since aircraft trajectories are influenced by numerous complex factors, incorporating these factors into the generation process is essential to improve the fidelity of the generated trajectories. This study employed a deep generative model to capture these factors, with surrounding traffic conditions selected as a representative example. In particular, a flow-based generative model was employed to obtain the generation probability of the trajectory to allow evaluation based on the probability of events occurring in the simulation. The generated trajectories were confirmed to be consistent with the operational flight envelope and surrounding traffic using publicly available track data.