Citation

Verdonk Gallego CE, Gómez Comendador VF, Sáez Nieto FJ, Orenga Imaz G, Arnaldo Valdés RM. Transp. Res. C Emerg. Technol. 2018; 95: 883-903.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.trc.2018.03.017

PMID

unavailable

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.

Language: en

Keywords

Air traffic control; Air traffic management; Artificial neural networks; BADA; Flows; Machine learning; Point-mass model; Trajectory prediction