Analysis of accident precursor data for Mid Air Collision occurrences using expert-build Bayesian Network model and Information Theory

Liang, Zhengyu and Arnaldo Valdes, Rosa Maria and Gomez Comendador, Victor Fernando and Roman Cordon, Ricardo (2019). Analysis of accident precursor data for Mid Air Collision occurrences using expert-build Bayesian Network model and Information Theory. In: "8th European Conference for Aeronautics and Space Science EUCASS 2019", 1-4 jul, Madrid, Spain. https://doi.org/10.13009/EUCASS2019-165.

Description

Title: Analysis of accident precursor data for Mid Air Collision occurrences using expert-build Bayesian Network model and Information Theory
Author/s:
  • Liang, Zhengyu
  • Arnaldo Valdes, Rosa Maria
  • Gomez Comendador, Victor Fernando
  • Roman Cordon, Ricardo
Item Type: Presentation at Congress or Conference (Article)
Event Title: 8th European Conference for Aeronautics and Space Science EUCASS 2019
Event Dates: 1-4 jul
Event Location: Madrid, Spain
Title of Book: 8th European Conference for Aeronautics and Space Science EUCASS 2019
Date: July 2019
Subjects:
Freetext Keywords: Aviation safety; ATM; loss of separation; mid-air collision; Bayesian network approach; information theory; entropy
Faculty: E.T.S. de Ingeniería Aeronáutica y del Espacio (UPM)
Department: Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 57781
DC Identifier: http://oa.upm.es/57781/
OAI Identifier: oai:oa.upm.es:57781
DOI: 10.13009/EUCASS2019-165
Official URL: https://www.eucass.eu/doi/EUCASS2019-0165.pdf
Deposited by: Memoria Investigacion
Deposited on: 17 Feb 2021 09:12
Last Modified: 17 Feb 2021 09:12
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