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Aerospace 2018, 5(2), 53; https://doi.org/10.3390/aerospace5020053

Simulation of Random Events for Air Traffic Applications

1
Ecole nationale de l’aviation civile (ENAC), Université Fédérale de Toulouse, 7 Avenue Edouard Belin, FR-31055 Toulouse CEDEX, France
2
Office Nationale d’études et de recherches aérospatiales (ONERA), DTIM, 2 Avenue Edouard Belin, FR-31055 Toulouse CEDEX 4, France
3
Swiss Air Navigation Services Ltd. (Skyguide), Route de Pré-Bois 15-17, CH-1215 Geneva 15, Switzerland
*
Author to whom correspondence should be addressed.
Received: 23 March 2018 / Revised: 27 April 2018 / Accepted: 28 April 2018 / Published: 3 May 2018
(This article belongs to the Collection Air Transportation—Operations and Management)
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Abstract

Resilience to uncertainties must be ensured in air traffic management. Unexpected events can either be disruptive, like thunderstorms or the famous volcano ash cloud resulting from the Eyjafjallajökull eruption in Iceland, or simply due to imprecise measurements or incomplete knowledge of the environment. While human operators are able to cope with such situations, it is generally not the case for automated decision support tools. Important examples originate from the numerous attempts made to design algorithms able to solve conflicts between aircraft occurring during flights. The STARGATE (STochastic AppRoach for naviGATion functions in uncertain Environment) project was initiated in order to study the feasibility of inherently robust automated planning algorithms that will not fail when submitted to random perturbations. A mandatory first step is the ability to simulate the usual stochastic phenomenons impairing the system: delays due to airport platforms or air traffic control (ATC) and uncertainties on the wind velocity. The work presented here will detail algorithms suitable for the simulation task. View Full-Text
Keywords: fast time traffic simulator; Gaussian field simulation; air traffic management fast time traffic simulator; Gaussian field simulation; air traffic management
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Puechmorel, S.; Dufour, G.; Fèvre, R. Simulation of Random Events for Air Traffic Applications. Aerospace 2018, 5, 53.

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