Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts
This work addresses the need for enhanced automation in conflict detection and resolution for air traffic management, but it is incremental as it applies existing deep learning techniques to a specific domain problem.
The paper tackled the problem of predicting when and how Air Traffic Controllers react to resolve conflicts in air traffic management, using deep learning methods that achieved very high accuracy on real-world datasets.
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.