Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
A Novel Predictability Performance Metric and Its Forecast Using Machine Learning Techniques
Year:2018
Research Areas
  • Mechanical aeronautics and naval engineering
Information
Abstract
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.
International
Si
Congress
37th AIAA/IEEE Digital Avionics Systems Conference (DASC)
960
Place
Londres
Reviewers
Si
ISBN/ISSN
978-1-5386-4112-5
Start Date
24/09/2018
End Date
27/09/2018
From page
730
To page
739
Proceedings 37th AIAA/IEEE Digital Avionics Systems Conference (DASC)
Participants
  • Autor: Ana del Rocio Barragan Montes (UPM)
  • Autor: Victor Fernando Gomez Comendador (UPM)
  • Autor: Rosa Maria Arnaldo Valdes (UPM)
  • Autor: Luis Perez Sanz (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Navegación Aérea
  • Departamento: Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos
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