A Supervised Learning Approach for 4D Air Traffic Conflict Prediction under Trajectory Uncertainty
Mohamed Arif Mohamed,
Phuoc Huu Dang,
Sameer Alam
Abstract:This paper presents a Supervised Learning approach for the problem of air traffic conflict prediction in 4dimensional space (3-dimensional space and time) under trajectory uncertainties, resulting in non-nominal conflict points. Decision support systems for conflict prediction offer shortterm conflict alerts, triggering alarms within a two-four-minute window before loss of separation (LOS), while medium-term conflicts are flagged eight to twelve minutes prior to LOS. However, the underlying models rely on flig… Show more
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