Predicting Aircraft Descent Length with Machine Learning

Richard Alligier (1) , David Gianazza (1, 2) , Nicolas Durand (1)

1 MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
2 IRIT-APO - Algorithmes Parallèles et Optimisation

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.

Mots clés: aircraft trajectory prediction; descent; BADA; Machine Learning

Domaines: Automatique / Robotique

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