|
Descripción
|
|
|---|---|
| The Air Tra?c 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 a?ected 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 Tra?c Management system may be exploited to understand to what extent AirTra?c Control actions impact on the vertical pro?le of ?ight trajectories.This paper analyses the impact of diverse operational factors on the vertical pro?le of ?ight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identi?cation 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 Arti?cial Neural Networks.Air Tra?c Control vertical operational procedures do not constitute a main factor impacting on the vertical pro?le of ?ight trajectories, once the top of descent is established. Additionally,airspace ?ows and the ?ight 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. | |
|
Internacional
|
Si |
|
JCR del ISI
|
Si |
|
Título de la revista
|
Transportation Research Part C: Emerging Technologies |
|
ISSN
|
0968-090X |
|
Factor de impacto JCR
|
3,968 |
|
Información de impacto
|
JIF 2017 |
|
Volumen
|
95 |
|
DOI
|
10.1016/j.trc.2018.03.017 |
|
Número de revista
|
|
|
Desde la página
|
883 |
|
Hasta la página
|
903 |
|
Mes
|
OCTUBRE |
|
Ranking
|
Q1 6/35 Transportation Science & Technology |