Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors

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,...

Full description

Saved in:
Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 20; no. 12; p. 969
Main Authors Arnaldo Valdés, Rosa, Liang Cheng, Schon, Gómez Comendador, Victor, Sáez Nieto, Francisco
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 14.12.2018
MDPI
Subjects
Accidents
Air traffic controllers
Aircraft accidents & safety
Aviation
aviation safety
Bayesian analysis
Bayesian network approach
entropy
Information theory
Investigations
loss of separation
Losses
Midair collisions
Precursors
Risk assessment
Taxonomy
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1099-4300
1099-4300
DOI:10.3390/e20120969