LAUSR

Abstract Airspace conflict resolution is critical for safe operations of aircraft, becoming more important with ever-increasing airspace congestion. While there are numerous aircraft's conflict resolution approaches in the literature, almost all of them are based on flight dynamics to predict aircraft's future trajectories and generate a conflict resolution strategy by maneuvering and thus modifying flight paths. However, it is unclear how to analyze the current-day operations provided by air traffic controllers from the flight dynamics viewpoint. In this paper, we propose a data-driven resolution generator (D2RG) for air traffic control using machine learning, which guarantees safety. In the D2RG, a resolution strategy for a given conflict situation can be automatically synthesized based on the knowledge about the types and characteristics (or parameters) of resolutions managed by air traffic controllers, which is extracted from flight data. The proposed methodology is demonstrated with flight data from a multi-fidelity modeling and simulation system, and also tested with actual flight data to show its applicability to real scenarios.