000344742 001__ 344742
000344742 005__ 20230827173512.0
000344742 0247_ $$2CORDIS$$aG:(EU-Grant)101114820$$d101114820
000344742 0247_ $$2CORDIS$$aG:(EU-Call)HORIZON-SESAR-2022-DES-ER-01$$dHORIZON-SESAR-2022-DES-ER-01
000344742 0247_ $$2originalID$$acorda_____he::101114820
000344742 035__ $$aG:(EU-Grant)101114820
000344742 150__ $$aArtificial Intelligence controller able to manage Air traffic Control (ATC) and Air Traffic Flow Management (ATFM) within a single framework$$y2023-06-01 - 2025-11-30
000344742 372__ $$aHORIZON-SESAR-2022-DES-ER-01$$s2023-06-01$$t2025-11-30
000344742 450__ $$aHYPERSOLVER$$wd$$y2023-06-01 - 2025-11-30
000344742 5101_ $$0I:(DE-588b)5098525-5$$2CORDIS$$aEuropean Union
000344742 680__ $$aAir Traffic Flow Management (ATFM) is the problem of adjusting the traffic demand in each traffic volume using ATFM measures so that aircraft can be safely separated during the subsequent Air Traffic Control (ATC) process. On the other hand, ATC officers (ATCOs) give different aircraft heading, speed, and flight level change instructions to separate them in flight. Both ATFM and ATC problems have been subject of research during decades, however, all previous works addressed the ATFM and ATC problems independently. The project aims to develop an HyperSolver based on advanced Artificial Intelligent Reinforcement Learning method with continuous reassessment and dynamic updates, i.e. an holistic solver from end-to-end, covering the whole process to manage, density of aircraft, complexity of trajectories, interactions (potential conflict in Dynamic Capacity Balancing timeframe) of trajectories, conflict of trajectories at medium-term and conflict of trajectories at short-term.
000344742 909CO $$ooai:juser.fz-juelich.de:1014102$$pauthority:GRANT$$pauthority
000344742 909CO $$ooai:juser.fz-juelich.de:1014102
000344742 980__ $$aG
000344742 980__ $$aCORDIS
000344742 980__ $$aAUTHORITY