A new trans-Atlantic route structure for strategic flight planning over the NAT airspace

• Published in: 2017 IEEE Congress on Evolutionary Computation (CEC)
• Main Authors: Dhief, Imen, Dougui, Nour Elhouda, Delahaye, Daniel, Hamdi, Noureddine
• Other Authors: Heterogeneous Advanced Networking and Applications Manouba (HANAlab), Université de la Manouba Tunisie (UMA)-École Nationale des Sciences de l'Informatique Manouba (ENSI), Université de la Manouba Tunisie (UMA), Ecole Nationale de l'Aviation Civile (ENAC)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2017
• Subjects: optimization model; wind-optimal flight trajectories; jetstreams; wind-optimal routes; induced delays; conflict resolution; trans-Atlantic route structure; strategic flight planning; NAT airspace; North Atlantic airspace; trans-Atlantic air traffic management; radar coverage; automated dependent surveillance-broadcast systems; separation standards; aircraft; oceanic flights; [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]; North Atlantic
• Online Access: https://hal-enac.archives-ouvertes.fr/hal-01587253; https://hal-enac.archives-ouvertes.fr/hal-01587253/document; https://hal-enac.archives-ouvertes.fr/hal-01587253/file/ImenDhiefCEC.pdf; https://doi.org/10.1109/CEC.2017.7969482

International audience Air traffic across the North Atlantic airspace has witnessed an incessant increase over the last decades. However, the efficiency of trans-Atlantic air traffic management is still low nowadays due to the limited radar coverage. Automated Dependent Surveillance-Broadcast systems represents an opportunity to enhance the strategic flight planning over the oceans by reducing separation standards between aircraft. Besides, the strong winds present a challenge for oceanic flights. Therefore, flying on the wind-optimal routes will save significantly both fuel and time. In this paper, we propose a new trans-Atlantic route structure that benefits from the jetstreams in order to construct wind-optimal flight trajectories. Then, we introduce an optimization model for detecting and resolving conflicts. The analysis is carried out on real traffic data to prove the efficiency of the proposed method. Experimental findings show an improvement in terms of conflict resolution and induced delays.