Artificial Intelligence controller able to manage Air traffic Control (ATC) and Air Traffic Flow Management (ATFM) within a single framework

ended

Air Traffic Flow Management (ATFM) is the problem of adjusting the traffic demand in each traffic volume using ATFM measures so that aircraft can be safely separated during the subsequent Air Traffic Control (ATC) process. On the other hand, ATC officers (ATCOs) give different aircraft heading, speed, and flight level change instructions to separate them in flight. Both ATFM and ATC problems have been subject of research during decades, however, all previous works addressed the ATFM and ATC problems independently. The project aims to develop an HyperSolver based on advanced Artificial Intelligent Reinforcement Learning method with continuous reassessment and dynamic updates, i.e. an holistic solver from end-to-end, covering the whole process to manage, density of aircraft, complexity of trajectories, interactions (potential conflict in Dynamic Capacity Balancing timeframe) of trajectories, conflict of trajectories at medium-term and conflict of trajectories at short-term.