Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts. (arXiv:2205.09539v1 [cs.AI])

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, …

ai air traffic arxiv data data-driven prediction traffic