Transferable aircraft trajectory prediction with generative deep imitation learning

Trajectory-oriented transformations to air traffic management operations require high fidelity aircraft trajectory prediction capabilities. Data-driven trajectory prediction approaches provide promising results, albeit with important limitations that hinder seriously the efficient and effective deployment of trajectory prediction methods: They need abundant training effort with a large amount of training samples and require training distinct models for different origin–destination (OD) airport pairs. In this paper, we address the problem of building transferable trajectory prediction models, casting the prediction problem as a transferable imitation task, introducing a novel formulation which (a) provides the capability to utilize trained models, in new OD pairs, offering a warm starting for computationally efficient training, and (b) improves the efficacy of data-driven trajectory prediction. The proposed approach provides very accurate results for large look-ahead time predictions, even if transferable models have been trained with few samples.