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.
Highlights
During the last decade, mid-air collisions (MAC) between large commercial aircraft have been rare events, maintaining safe separation between aircraft is one of the key aviation safety challenges as the new generation of air traffic management (ATM) systems (SESAR and NextGen) develops
The authors have developed a method that combines principles from quantitative risk analysis, Bayesian modeling, and information theory, to infer the likelihood of catastrophic accidents based upon precursor data
The application of this methodology is based upon the principle that major accidents and their links to near accidents arise from common initiating events and descriptive factors
Summary
During the last decade, mid-air collisions (MAC) between large commercial aircraft have been rare events, maintaining safe separation between aircraft is one of the key aviation safety challenges as the new generation of air traffic management (ATM) systems (SESAR and NextGen) develops. In the absence of sufficient accident direct data, precursor-based probabilistic risk analysis methods are considered a promising and efficient tool for this purpose [2]. The widely-accepted definition of an accident precursor is an event with no catastrophic or severe consequences but that could have developed into an accident if additional safety barriers had failed [3,4,5,6,7]
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