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Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Abstract : Airport capacity has become a constraint in the air transportation networks, due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum the potential conflicts. In this framework, airspace together with ground airport operations are considered. Conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways, and as capacity overloads for taxiway network and terminals. The methodology proposed in this work consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, this is supported by a case study of Paris Charles de Gaulle Airport.
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Submitted on : Thursday, October 24, 2019 - 5:19:53 PM
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Paolo Maria Scala, Miguel Antonio Mujica Mota, Ji Ma, Daniel Delahaye. Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management. IEEE Transactions on Intelligent Transportation Systems, IEEE, 2020, 21 (8), pp. 3233-3246. ⟨10.1109/TITS.2019.2924981⟩. ⟨hal-02166833⟩



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