Uncertainty-Aware Conflict Detection Algorithm and Conflict Area Visualization for Air Traffic Controllers

Current air traffic management (ATM) systems face rising challenges due to increasing volume of air traffic and shortage of air traffic controllers (ATCOs). Medium-term conflict detection (MTCD) is an important tool to assist ATCOs. Traditional MTCD usually relies on deterministic, trajectory-based models. Hence, uncertainties in flight movements are not considered, potentially leading to false alarms or missed conflicts. Therefore, this paper presents an uncertainty-aware MTCD algorithm and a dynamic representation of conflict areas in ATCO displays. The suggested area-based MTCD approach employs ellipsoidal models to define protected zones around aircraft to identify spatial and temporal overlap zones rather than just points along trajectories. By dynamically adjusting ellipsoid dimensions with Monte Carlo simulations based on variations of wind, aircraft speed, and pilot reaction delays, our model provides a probabilistic MTCD. A horizontal and vertical conflict display augments colour-coded risk indicators for sub-zones of conflict areas to enhance ATCOs’ situation awareness. An evaluation with ten ATM experts – including two ATCOs – in a simulated air traffic environment showed conceptual soundness and usability. Participants rated the MTCD model’s concept with a score of 8.2 on average on a 10-point scale with high scores in favour. Map visualizations were well received, with all ratings being 8 or more. The evaluation as well identified potential improvements for pilot response modelling and computational speed to advance MTCD and its visualization in future ATM systems.