Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning |
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| Affiliation: | 1. Aeronautical Systems, Air Transport and Airports Department, Universidad Politécnica de Madrid, Madrid, Spain;2. Centre for Aeronautics, School of Aerospace, Transport and Manufacturing. Cranfield University, Cranfield, United Kingdom;3. CRIDA A.I.E., Madrid, Spain;1. College of Civil Aviation, Nanjing Aeronautics and Astronautics University, Nanjing 211106, China;2. China Electronics Technology Group Corporation, 28th, Nanjing 210007, China;3. Civil and Environmental Engineering, University of South Florida, Tampa 33620, USA;1. Faculty of Engineering, Environment and Computing, Coventry University, Priory Street, Coventry CV1 5FB, United Kingdom;2. Solid Earth Physics Institute, Faculty of Physics, National and Kapodistrian University of Athens, Panepistimiopolis, Zografos 157 84, Athens, Greece;1. Slovenia Control Ltd., Zgornji Brnik 130n, SI-4210 Brnik-Aerodrom, Slovenia;2. Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia;1. Ecole National de l’Aviation Civile, Département de Mathématique Appliquées, Informatique et Automatique pour l’Aérien, 7, Avenue Edouard Belin, 31055 Toulouse, France;2. Institut National des Sciences Appliquées, Département de Génie Mathématique et Modélisation, 135, Avenue de Rangueil, 31077 Toulouse, France |
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| Abstract: | The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase. |
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| Keywords: | Trajectory prediction Air traffic management Air traffic control Point-mass model Flows Machine learning Artificial neural networks BADA |
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