Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
A Machine Learning approach to Air Traffic Interdependency modelling and its application to Trajectory Prediction
Año:2019
Áreas de investigación
  • Control del tráfico aéreo,
  • Gestión del tráfico aéreo,
  • Vuelo
Datos
Descripción
Air Traffic Management is evolving towards a Trajectory-Based Operations paradigm. Trajectory prediction will hold a key role supporting its deployment, but it is limited by a lack of understanding of air traffic associated uncertainties, specifically contextual factors. Trajectory predictors are usually based on modelling aircraft dynamics based on intrinsic aircraft features. These aircraft operate within a known air route structure and under given meteorological conditions. However, actual aircraft trajectories are modified by the air traffic control depending on potential conflicts with other traffics. This paper introduces surrounding air traffic as a feature for ground-based trajectory prediction. The introduction of air traffic as a contextual factor is addressed by identifying aircraft which are likely to lose the horizontal separation. For doing so, this paper develops a probabilistic horizontal interdependency measure between aircraft supported by machine learning algorithms, addressing time separations at crossing points. Then, vertical profiles of flight trajectories are characterised depending on this factor and other intrinsic features. The paper has focused on the descent phase of the trajectories, using datasets corresponding to an en-route Spanish airspace volume. The proposed interdependency measure demonstrates to identify in advance conflicting situations between pairs of aircraft for this use case. This is validated by identifying associated air traffic control actions upon them and their impact on the vertical profile of the trajectories. Finally, a trajectory predictor for the vertical profile of the trajectory is developed, considering the interdependency measure and other operational factors. The paper concludes that the air traffic can be included as a factor for the trajectory prediction, impacting on the location of the top of descent for the specific case which has been studied.
Internacional
Si
JCR del ISI
Si
Título de la revista
Transportation Research Part C-Emerging Technologies
ISSN
0968-090X
Factor de impacto JCR
5,775
Información de impacto
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2018 Journal Citation Reports (Clarivate Analytics, 2019)
Volumen
107
DOI
10.1016/j.trc.2019.08.015
Número de revista
Desde la página
356
Hasta la página
386
Mes
OCTUBRE
Ranking
Q1 3/35
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Cristian Eduardo Verdonk Gallego (UPM)
  • Autor: Victor Fernando Gomez Comendador (UPM)
  • Autor: Manuel Ángel Amaro Carmona (Cranfield University)
  • Autor: Rosa Maria Arnaldo Valdes (UPM)
  • Autor: Fco. Javier Saez Nieto (UPM)
  • Autor: Miguel García Martínez (CRIDA A.I.E.)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos
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