Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
Year:2018
Research Areas
  • Mechanical aeronautics and naval engineering
Information
Abstract
This paper combines Bayesian networks (BN) and information theory to model the likelihood of severe loss of separation (LOS) near accidents, which are considered mid-air collision (MAC) precursors. BN is used to analyze LOS contributing factors and the multi-dependent relationship of causal factors, while Information Theory is used to identify the LOS precursors that provide the most information. The combination of the two techniques allows us to use data on LOS causes and precursors to define warning scenarios that could forecast a major LOS with severity A or a near accident, and consequently the likelihood of a MAC. The methodology is illustrated with a case study that encompasses the analysis of LOS that have taken place within the Spanish airspace during a period of four years
International
Si
JCR
Si
Title
Entropy
ISBN
1099-4300
Impact factor JCR
1,821
Impact info
Datos JCR del año 2016
Volume
20
10.3390/e20120969
Journal number
From page
969
To page
988
Month
SIN MES
Ranking
Participants
  • Autor: Rosa Maria Arnaldo Valdes (UPM)
  • Autor: Schon Liang Chen (UPM)
  • Autor: Victor Fernando Gomez Comendador (UPM)
  • Autor: Francisco Saez Nieto (Cranfield University)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Navegación Aérea
  • Departamento: Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)