Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts.

Abstract

With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, this article proposes deep learning (DL) techniques that model Air Traffic Controllers’ reactions in resolving conflicts violating aircraft trajectories separation minimum constraints: This implies learning when the Air Traffic Controller reacts towards resolving a conflict, and how he/she reacts. Timely reactions, to which this article aims, focus on when do reactions happen, aiming to predict the trajectory points, as the aircraft state evolves, that the Air Traffic Controller (ATCO) issues a conflict resolution action. Towards this goal, the article formulates the Air Traffic Controllers’ reaction prediction problem for CD&R, presents DL methods that can model Air Traffic Controllers’ timely reactions, and evaluates these methods in real-world data sets, showing their efficacy in solving the problem with very high accuracy.