A Catalogue of Deconfliction Actions Extracted from Historical ADS-B Data

Abstract

A conflict in Air Traffic Management is defined by a potential future risk of loss of separation. To solve a conflict, air traffic controllers take proactive actions to ensure safe separations between aircraft. They issue specific instructions to pilots for corrective measures, such as lateral manoeuvres, changes in altitude or speed adjustments. To alleviate the workload of controllers, the conflict detection and resolution process can be automated, resulting in recommendations for efficient manoeuvres. Traditional conflict resolution algorithms often neglect factors inherent to controllers' decision-making, leading to seemingly impractical manoeuvre suggestions from a human standpoint, causing reluctance in acceptation among controllers. The aim of our research is to obtain a catalogue of prevalent deconfliction practices, incorporating controllers' uncertainty models derived from actual flight data. In the current contribution, we focus on lateral deconfliction manoeuvres in en-route air traffic, and implement a simple heuristic method to extract a catalogue of resolved conflict situations from historical ADS-B data. This catalogue will provide insights into controllers' decision-making processes. In future works, we intend to use this catalogue to identify the best practices for traffic deconfliction, taking into account human factors and operational uncertainties, and to incorporate them into conflict resolution algorithms.