Machine Learning Applied to Aispeed Prediction During Climb

Richard Alligier, David Gianazza, Nicolas Durand

11th USA/Europe Air Traffic Management Research and Developpment Seminar

2015/06/23

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Abstract:

In this paper, we apply Machine Learning methods to improve the aircraft climb prediction in the context of ground- based applications. Mass and speed intent are key parameters for climb prediction. As they are considered as competitive parame- ters by many airlines, they are currently not available to ground- based trajectory predictors. Consequently, most predictors today use reference parameters that may be quite different from the actual ones. In our most recent paper ([1]), we have demonstrated that Machine Learning techniques provide a mass estimation signif- icantly more precise than two state-of-the-art mass estimation methods. In this paper, we apply similar techniques to the speed intent. We first build a set of examples by adjusting CAS/Mach speed profile to each climb trajectory in our database. Then, using the adjusted values ( c cas; c M ) in this database, we learn a model able to predict the ( cas; M ) values of a new trajectory, using its past points as input. We apply this technique to actual Mode-C radar data and we consider 9 different aircraft types. When compared with the reference speed profiles provided by BADA, the reduction of the speed RMSE ranges from 36 % to 79 %, depending on the aircraft type. Using the predicted mass and speed profile, BADA is used to compute the predicted future trajectory with a 10 minute horizon. When compared with BADA used with the reference parameters, the reduction of the future altitude RMSE ranges from 45 % to 87 %

Keywords: Trajectory Prediction

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BibTeX entry:

@InProceedings{atm2015_alligiergianazzadurand,
 title = {Machine Learning Applied to Aispeed Prediction During Climb},
 author = {Richard Alligier and David Gianazza and Nicolas Durand},
 BookTitle = {11th USA/Europe Air Traffic Management Research and Developpment Seminar},
 year = {2015}
}

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