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A Distributed Metaheuristic Approach for Complexity Reduction in Air Traffic for Strategic 4D Trajectory Optimization

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Paveen Juntama
Supatcha Chaimatanan
  • Fonction : Auteur
  • PersonId : 946090
Sameer Alam
  • Fonction : Auteur
Daniel Delahaye

Résumé

This paper presents a new challenge on the strategic 4D trajectory optimization problem with the evaluation of air traffic complexity by using the geometric-based intrinsic complexity measure called König metric. The demonstration of König metric shows the potential that the algorithm can capture the disorganized the disorganized traffic which represents the difficulty of maintaining situational awareness as expected by the air traffic controller. We reformulate the optimization problem with two trajectory separation approaches including delaying flight departure time and allocating the new flight level subject to limited delay time of departure, limited changes of flight levels and fuel consumption constraints. We propose our solution to solve daily traffic demands in the regional French airspace. The resolution process uses the distributed metaheuristic algorithm to optimize aircraft trajectories in 4D environment with the objective of finding the optimal air traffic complexity. The experimental results shows the reduction of maximum complexity more than 95 % with average delay of 2.69 minutes. The optimized trajectories can save fuel more than 80000 kg. The proposed algorithm not only reduces the air traffic complexity but also maintain its distribution in traffic. The research results represent further steps towards taking other trajectory separations methods and aircraft trajectory uncertainties into account, developing our approach at the continental scale as well as adapting it in the pre-tactical and tactical planning phase.
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hal-02547502 , version 1 (21-04-2020)

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Paveen Juntama, Supatcha Chaimatanan, Sameer Alam, Daniel Delahaye. A Distributed Metaheuristic Approach for Complexity Reduction in Air Traffic for Strategic 4D Trajectory Optimization. AIDA-AT 2020, 1st conference on Artificial Intelligence and Data Analytics in Air Transportation, Feb 2020, Singapore, Singapore. pp.1-9 / ISBN : 978-1-7281-5381-0, ⟨10.1109/AIDA-AT48540.2020.9049200⟩. ⟨hal-02547502⟩
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