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

TL;DR

This paper develops deep learning models to predict Air Traffic Controllers' timely reactions to conflicts, aiming to improve automation in conflict detection and resolution within air traffic management.

Contribution

It introduces a novel formulation of the ATCO reactions prediction problem and demonstrates effective deep learning solutions on real-world data.

Findings

High accuracy in predicting ATCO reactions

Effective modeling of conflict resolution actions

Improved automation potential in air traffic management

Abstract

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.