Predicting Aircraft Descent Length with Machine Learning

Abstract : Predicting aircraft trajectories is a key element in the detection and resolution of air traffic conflicts. In this paper, we focus on the ground-based prediction of final descents toward the destination airport. Several Machine Learning methods – ridge regression, neural networks, and gradient-boosting machine – are applied to the prediction of descents toward Toulouse airport (France), and compared with a baseline method relying on the Eurocontrol Base of Aircraft Data (BADA). Using a dataset of 15,802 Mode-S radar trajectories of 11 different aircraft types, we build models which predict the total descent length from the cruise altitude to a given final altitude. Our results show that the Machine Learning methods improve the root mean square error on the predicted descent length of at least 20 % for the ridge regression, and up to 24 % for the gradient-boosting machine, when compared with the baseline BADA method.
Type de document :
Communication dans un congrès
ICRAT 2016, 7th International Conference on Research in Air Transportation, Jun 2016, Philadelphia, United States
Liste complète des métadonnées

Littérature citée [33 références]  Voir  Masquer  Télécharger

https://hal-enac.archives-ouvertes.fr/hal-01353960
Contributeur : Laurence Porte <>
Soumis le : mardi 16 août 2016 - 14:27:38
Dernière modification le : mercredi 12 septembre 2018 - 17:46:03
Document(s) archivé(s) le : jeudi 17 novembre 2016 - 10:55:30

Fichier

icrat2016.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01353960, version 1

Citation

Richard Alligier, David Gianazza, Nicolas Durand. Predicting Aircraft Descent Length with Machine Learning . ICRAT 2016, 7th International Conference on Research in Air Transportation, Jun 2016, Philadelphia, United States. 〈hal-01353960〉

Partager

Métriques

Consultations de la notice

267

Téléchargements de fichiers

210