Three previous workshops of this thematic challenge (TC2) addressed different approaches to improve trajectory prediction and management through data-driven techniques. Whilst some of these approaches involved probabilistic methods and statistical signal processing, machine learning accounted for the majority of techniques pursued in TC2. At the same time, machine learning approaches are applied in other ATM application areas so that exploiting the synergies between these different application areas seems desirable. The objectives of this workshop are to bring together researchers from different Engage and SESAR Exploratory Research projects, and a selection of Engage PhDs, applying machine learning for trajectory prediction and also broader application areas, to identify best practices, similarities and synergies.

Further details, including event programmes, will be updated on the Engage website (see under "Participate") as they become available.