Memorias de investigación
Ponencias en congresos:
A Novel Predictability Performance Metric and Its Forecast Using Machine Learning Techniques
Año:2018

Áreas de investigación
  • Ingeniería mecánica, aeronaútica y naval

Datos
Descripción
Trajectory predictability is a paramount cornerstone of trajectory-based operations. The uncertainty in the four-dimensional position of aircraft affects the number of flights that the Air Traffic Control service is able to manage. Consequently, airspace capacity is directly impacted by a poor predictability performance. This paper presents a methodology that forecasts predictability performance in pre-tactical phase for traffic flows. The methodology will allow the Network Manager to establish preventive measures to avoid undesired impact on the flow of traffic in the event of predictability degradation. Moreover, if a lack of predictability is recurrently detected for a volume of airspace, strategic measures could be taken to optimise airspace design.
Internacional
Si
Nombre congreso
37th AIAA/IEEE Digital Avionics Systems Conference (DASC)
Tipo de participación
960
Lugar del congreso
Londres
Revisores
Si
ISBN o ISSN
978-1-5386-4112-5
DOI
Fecha inicio congreso
24/09/2018
Fecha fin congreso
27/09/2018
Desde la página
730
Hasta la página
739
Título de las actas
Proceedings 37th AIAA/IEEE Digital Avionics Systems Conference (DASC)

Esta actividad pertenece a memorias de investigación

Participantes

Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Navegación Aérea
  • Departamento: Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos