  With the aim to enhance automation in conflict detection and resolution
(CD&R) tasks in the Air Traffic Management domain, in this paper we propose
deep learning techniques (DL) that can learn models of Air Traffic Controllers'
(ATCO) reactions in resolving conflicts that can violate separation minimum
constraints among aircraft trajectories: This implies learning when the ATCO
will react towards resolving a conflict, and how he/she will react. Timely
reactions, to which this paper aims, focus on when do reactions happen, aiming
to predict the trajectory points, as the trajectory evolves, that the ATCO
issues a conflict resolution action, while also predicting the type of
resolution action (if any). Towards this goal, the paper formulates the ATCO
reactions prediction problem for CD&R, and presents DL methods that can model
ATCO timely reactions and evaluates these methods in real-world data sets,
showing their efficacy in prediction with very high accuracy.
