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Quantitative Assessment of Minor Incidents to Accident Transformation Probability and Its Impact on Aerodrome Operations
ISSN: 2327-5626, e-ISSN: 2327-5634
Published June 10, 2021 by SAE International in United States
Citation: Skorupski, J. and Rutkowska, P., "Quantitative Assessment of Minor Incidents to Accident Transformation Probability and Its Impact on Aerodrome Operations," SAE Int. J. Trans. Safety 9(2):2021.
Numerous operational procedures regulate aerodrome ground traffic. Detailed solutions in these procedures often come from preventive recommendations formulated as a result of accident cause analysis. With time, the conclusions drawn based on incidents, i.e., events that did not result in material damage or casualties, are becoming increasingly significant. In this article, we propose a new method for determining the probability of an incident turning into an air accident, based on the example of aerodrome traffic operations. Premises conducive to an accident in the considered class of events depend on both human and physical factors. Thus a hybrid approach was applied. We used a fuzzy inference system to analyze the premises dependent on vehicle operators, while the simulation method was selected to examine the premises dependent on physical factors. Both were integrated using the technique of event trees with fuzzy probabilities (ETFP). With the developed methodology, it is possible to conduct a Quantitative Assessment of Incident to Accident Transformation probability (QAIAT). We used the QAIAT method to analyze the real event in aerodrome traffic. As part of the experiments, we demonstrated the accident probability’s quantitative dependence on Ground Support Equipment (GSE) vehicle operator training, traffic conditions, and braking parameters. We showed how to use this type of analysis to assess the effectiveness of the existing operating procedures and their possible modification. Comparing the likelihood of an incident turning into an accident before and after applying these procedures gives us significant knowledge about the existing or designed solutions’ effectiveness.