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Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction

Alligier, Richard and Gianazza, David and Durand, Nicolas Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction. (2015) IEEE Transactions on Intelligent Transportation Systems, 16 (6). 3138-3149. ISSN 1524-9050

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Official URL: http://dx.doi.org/10.1109/TITS.2015.2437452


In this paper, we apply Machine Learning methods to improve the aircraft climb prediction in the context of ground-based applications. Mass is a key parameter for climb prediction. As it is considered a competitive parameter by many airlines, it is currently not available to ground-based trajectory predictors. Consequently, most predictors today use a reference mass that may be different from the actual aircraft mass. In previous papers, we have introduced a least square method to estimate the mass from past trajectory points, using the physical model of the aircraft. Another mass estimation method, based on an adaptive mechanism, has also been proposed by Schultz et. al. We now introduce a new approach, where the mass is considered as the response variable of a prediction model that is learned from a set of example trajectories. This Machine Learning approach is compared with the results obtained when using the BADA (Base of Aircraft Data) reference mass or the two state-of-the-art mass estimation methods. In these experiments, 9 different aircraft types are considered. When compared with the baseline method (resp. the mass estimation methods), the Machine Learning approach reduces the RMSE (Root Mean Square Error) on the predicted altitude by at least 58 % (resp. 27 %) when assuming the speed profile to be known, and by at least 29 % (resp. 17 %) when using the BADA speed profile except for the aircraft types E145 and F100. For these types, the observed speed profile is far from the BADA speed profile.

Item Type:Article
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org This papers appears in Volume 16 Issue 6 IEEE Transactions on Intelligent Transportation Systems. Print ISSN: 1524-9050 The original PDF of the article can be found at: http://ieeexplore.ieee.org/document/7123640/ Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Ecole Nationale de l'Aviation Civile - ENAC (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited On:19 Sep 2017 15:34

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