|
Citation
|
Delgado-Aguilera Jurado R, Gómez Comendador VF, Zamarreño Suárez M, Pérez Moreno F, Verdonk Gallego CE, Arnaldo Valdés RM. Safety Sci. 2023; 163: e106101. |
|
Abstract
|
[SafetyLit note: ATM = air traffic management]
Due to the high level of safety achieved in ATM, relatively little data is available on negative safety outcomes. For this reason, the current knowledge of the determinants of the occurrence of loss of separation incidents between aircraft is limited, as is the industry's current ability to predict them. For this reason, a predictive methodology was developed with the aim of characterising safety in airspace between en-route aircraft and to enable the identification of the most influential factors in terms of Separation Minima Infringements (SMI). For this purpose, Bayesian Networks were selected for their high predictive capacity to estimate low probability events and for allowing the integration of knowledge modelling with data inference. Thus, a conceptual framework was established to integrate the current knowledge available on causality and SMI precursors with the hindsight derived from the analysis of the type of data available. The main objective of this paper is to perform a sensitivity analysis on the model with the intention of drawing conclusions about the most influential variables in the possible breach of ATM safety barriers that could lead to the occurrence of a SMI.
Highlights
• The most influential factors in the progression of a possible loss of separation between en-route aircraft are analysed.
• Air Traffic Controllers (ATCo) play a fundamental role in ensuring the safety of aircraft.
• Bayesian networks provide high predictive power for estimating low probability events and allow the integration of knowledge modelling with data inference.
• A predictive methodology is developed with the objective of characterising airspace safety between en-route aircraft in terms of Separation Minima Infringements (SMI).
Language: en |