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Hyperheuristic Approach Based on Reinforcement Learning for Air Traffic Complexity Mitigation

Abstract : Airspace capacity has become a critical resource for air transportation. Complexity in traffic patterns is a structural problem, whereby airspace capacity is sometimes saturated before the number of aircraft has reached the capacity threshold. This paper addresses a strategic planning problem with an efficient optimization approach that minimizes traffic complexity based on linear dynamical systems in order to improve the traffic structure. Traffic structuring techniques comprise departure time adjustment, en route trajectory deviation, and flight-level allocation. The resolution approach relies on the hyperheuristic framework based on reinforcement learning to improve the searching strategy during the optimization process. The proposed methodology is implemented and tested with a full day of traffic in the French airspace. Numerical results show that the proposed approach can reduce air traffic complexity by 92.8%. The performance of the proposed algorithm is then compared with two different algorithms, including the random search and the standard simulated annealing. The proposed algorithm provides better results in terms of air traffic complexity and the number of modified trajectories. Further analysis of the proposed model was conducted by considering time uncertainties. This approach can be an innovative solution for capacity management in the future air traffic management system.
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https://hal-enac.archives-ouvertes.fr/hal-03668724
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Soumis le : lundi 16 mai 2022 - 08:59:43
Dernière modification le : jeudi 23 juin 2022 - 21:33:31

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Daniel Delahaye, Paveen Juntama, Supatcha Chaimatanan, Sameer Alam. Hyperheuristic Approach Based on Reinforcement Learning for Air Traffic Complexity Mitigation. Journal of Aerospace Information Systems, American Institute of Aeronautics and Astronautics, 2022, ⟨10.2514/1.I011048⟩. ⟨hal-03668724⟩

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