Deep Learning in Aeronautics: air TrafficTrajectory Classification Based on Weather Reports

• Néstor Jiménez-Campfens ^[1] ; Adrián Colomer ^[1] ; Javier Núñez ^[2] ; Mogollón, Juan M. ^[2] ; Rodríguez, Antonio L. ^[2] ; Valery Naranjo ^[1]
  1. [1] Universidad Politécnica de Valencia

     Universidad Politécnica de Valencia

     Valencia, España

     
  2. [2] Centro de Investigaciones Científicas Isla de la Cartuja

     Centro de Investigaciones Científicas Isla de la Cartuja

     Sevilla, España

     
• Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.), David Camacho Fernández (ed. lit.), Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 148-155
• Idioma: inglés
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• Resumen

  • New paradigms in aviation, as the expected shortage of qualified pilots and the increasing number of flights worldwide, present big challenges to aeronautic enterprises and regulators. In this sense, a concept known as Single Pilot Operations arises in the task of dealing with these challenges, for which, automation becomes necessary, especially in Air Traffic Management. In this regard, this paper presents a deep learning-based approach to leveraging the job of both ground controllers and pilots. Making use of Meteorological Terminal Air Reports, obtained regularly from every aerodrome worldwide, we created a model based on a multi-layer perceptron capable of determining the approach trajectory of an aircraft thirty minutes prior to the expected landing time. Experiments on aircraft trajectories from Toulouse to Seville, show an accuracy, recall and F1-score higher than 0.9 for the resultant predictive model.