Observatorio de I+D+i UPM

Memorias de investigación
Conferencias:
Analysis of accident precursor data for Mid Air Collision occurrences using expert-build Bayesian Network model and Information Theory
Año:2019
Áreas de investigación
  • Ingeniería aeronaútica,
  • Factores humanos en aeronáutica,
  • Operación aérea,
  • Transporte aéreo
Datos
Descripción
Causes leading to loss of Separation (LOS) in serious and major incidents are considered as potential precursors for Mid-Air Collision (MAC) accident. This paper attempts to model the likelihood of these precursors combining Bayesian Networks (BN), which are based on expert-built, and Information Theory (IT). BN provides the analysis of LOS contributing factors and the multi-dependent relationship of causal factors identified from real Air Traffic Management (ATM) incident reports, while IT contributes to the identification of LOS precursors providing the most information. The combination of these two techniques allows us using data on causes and precursors of LOS to define warning scenarios. These precursors could forecast a serious LOS with severity A and B, and consequently the likelihood of a MAC. The methodology is illustrated with a case study that encompasses the analysis of LOS severity A and B that have been notified within the Spanish airspace during a period of four years.
Internacional
Si
ISSN o ISBN
2493-3503
Entidad relacionada
Nacionalidad Entidad
Sin nacionalidad
Lugar del congreso
Madrid, Spain
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Schon Zhengyu Liang Cheng (UPM)
  • Autor: Rosa Maria Arnaldo Valdes (UPM)
  • Autor: Victor Fernando Gomez Comendador (UPM)
  • Autor: Ricardo Roman Cordon (UPM)
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
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