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.

Details

Title
Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
Author
Rosa María Arnaldo Valdés 1 ; Schon ZY Liang Cheng 2   VIAFID ORCID Logo  ; Gómez Comendador, Victor Fernando 1 ; Sáez Nieto, Francisco Javier 3   VIAFID ORCID Logo 

1  Department of Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos, School of Aerospace Engineering, Universidad Politécnica de Madrid (UPM), Plaza Cardenal Cisneros n3, 28040 Madrid, Spain 
2  Department of Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos, School of Aerospace Engineering, Universidad Politécnica de Madrid (UPM), Plaza Cardenal Cisneros n3, 28040 Madrid, Spain; Aeronautic, Space & Defence Division, ALTRAN Innovation S.L., Calle Campezo 128022 Madrid, Spain 
3  Centre for Aeronautics, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 OAL, UK 
First page
969
Publication year
2018
Publication date
2018
Publisher
MDPI AG
e-ISSN
10994300
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.3390/e20120969
ProQuest document ID
2582798520
Copyright
© 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.