Arrêt de service programmé du vendredi 10 juin 16h jusqu’au lundi 13 juin 9h. Pour en savoir plus
Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Abstract : Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of new automated tools. In current operations, trajectory prediction is computed using a physical model. It models the forces acting on the aircraft to predict the successive points of the future trajectory. Using such a model requires knowledge of the aircraft state (mass) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems. This paper focuses on the climb phase. We improve the trajectory prediction accuracy by predicting some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The 11 most frequent aircraft types are studied. The obtained data set contains millions of climbing segments from all over the world. The climbing segments are not filtered according to their altitude. Predictive models returning the missing parameters are learned from this data set, using a Machine Learning method. The trained models are tested on the two last months of the year and compared with a baseline method (BADA used with the mean parameters computed on the first ten months). Compared with this baseline, the Machine Learning approach reduce the RMSE on the altitude by 48 % on average on a 10 minutes horizon prediction. The RMSE on the speed is reduced by 25 % on average. The trajectory prediction is also improved for small climbing segments. Using only information available before the considered aircraft takeoff , the Machine Learning method can predict the unknown parameters, reducing the RMSE on the altitude by 25 % on average. The data set and the Machine Learning code are publicly available.
Liste complète des métadonnées

Littérature citée [46 références]  Voir  Masquer  Télécharger
Contributeur : Richard Alligier Connectez-vous pour contacter le contributeur
Soumis le : vendredi 21 septembre 2018 - 11:48:02
Dernière modification le : mercredi 3 novembre 2021 - 05:14:58
Archivage à long terme le : : samedi 22 décembre 2018 - 14:19:37


Fichiers produits par l'(les) auteur(s)




Richard Alligier, David Gianazza. Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study. Transportation research. Part C, Emerging technologies, Elsevier, 2018, 96, pp.72-95. ⟨10.1016/j.trc.2018.08.012⟩. ⟨hal-01878615⟩



Consultations de la notice


Téléchargements de fichiers