Air Traffic Structuration based on Linear Dynamical Systems
Résumé
This paper presents a novel air traffic structuration approach to maintain flows of air traffic and to adapt traffic situations, which can reduce the mental workload of air traffic controllers. We reformulate the optimization problem by reorganizing the aircraft trajectories in space (e.g. aircraft rerouting) or time dimension (e.g. rescheduling time of departure, flow crossing, time based metering) or both in some areas where the system identifies a high level of disorder in the traffic structure. To structure the traffic, an air traffic complexity metric based on linear dynamical systems is used for this optimization problem. To minimize the impact of traffic structure, we propose an adaptive metaheuristic approach with the integration of reinforcement learning for our resolution algorithm. The resolution algorithm is applied for short-term (flow crossing, time-based metering, and traffic encounters) trajectory planning applications and national scale planning under time uncertainty in French airspace. For short-term scenarios, our approach can restructure the traffic which allows controllers to take less effort for managing traffic situations. Our solution also improves the traffic structure with approximately 50 % reduction of air traffic complexity at national scale. Our re earch findings introduce further steps toward taking other trajectory structuration techniques into account and developing new search strategies to our resolution algorithm.
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