Cargando…
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,...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512568/ https://www.ncbi.nlm.nih.gov/pubmed/33266693 http://dx.doi.org/10.3390/e20120969 |
_version_ | 1783586188360155136 |
---|---|
author | Arnaldo Valdés, Rosa María Liang Cheng, Schon Z.Y. Gómez Comendador, Victor Fernando Sáez Nieto, Francisco Javier |
author_facet | Arnaldo Valdés, Rosa María Liang Cheng, Schon Z.Y. Gómez Comendador, Victor Fernando Sáez Nieto, Francisco Javier |
author_sort | Arnaldo Valdés, Rosa María |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7512568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75125682020-11-09 Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors Arnaldo Valdés, Rosa María Liang Cheng, Schon Z.Y. Gómez Comendador, Victor Fernando Sáez Nieto, Francisco Javier Entropy (Basel) Article 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. MDPI 2018-12-14 /pmc/articles/PMC7512568/ /pubmed/33266693 http://dx.doi.org/10.3390/e20120969 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Arnaldo Valdés, Rosa María Liang Cheng, Schon Z.Y. Gómez Comendador, Victor Fernando Sáez Nieto, Francisco Javier Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors |
title | Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors |
title_full | Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors |
title_fullStr | Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors |
title_full_unstemmed | Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors |
title_short | Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors |
title_sort | application of bayesian networks and information theory to estimate the occurrence of mid-air collisions based on accident precursors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512568/ https://www.ncbi.nlm.nih.gov/pubmed/33266693 http://dx.doi.org/10.3390/e20120969 |
work_keys_str_mv | AT arnaldovaldesrosamaria applicationofbayesiannetworksandinformationtheorytoestimatetheoccurrenceofmidaircollisionsbasedonaccidentprecursors AT liangchengschonzy applicationofbayesiannetworksandinformationtheorytoestimatetheoccurrenceofmidaircollisionsbasedonaccidentprecursors AT gomezcomendadorvictorfernando applicationofbayesiannetworksandinformationtheorytoestimatetheoccurrenceofmidaircollisionsbasedonaccidentprecursors AT saeznietofranciscojavier applicationofbayesiannetworksandinformationtheorytoestimatetheoccurrenceofmidaircollisionsbasedonaccidentprecursors |