Apriori algorithm for association rules mining in aircraft runway excursions

Abstract

Safety is one of the International Civil Aviation Organization’s (ICAO) strategic objectives to foster a global civil aviation system. Statistically, almost 40% of aviation accidents occur at airports, the most frequent type is runway excursion. The assessment of accident severity is an essential part of safety assessment methods. In this study, a set of influencing factors which would affect the severity of different types of runway excursions were investigated in order to determine those factors that typically occur together in runway excursion accidents. In order to achieve this aim, a large database was created, which contains information on all the conditions surrounding the runway excursion event, in a period between 2006 and 2016, for a total of 434 runway excursions. Associate Rules method with Apriori algorithm was used. The Apriori algorithm was used separately for each type of runway excursion. The results of this study show that different variables are associated with different types of runway excursions and different categories of severity. The most significant variable for all types of runway excursion is the class of the aircraft. Events with Major and Hazardous severity are associated with small aircraft, while events with catastrophic severity are associated with aircraft of medium-large dimensions. The least significant variable for runway excursion accidents is "Potential causes". The knowledge of the runway excursions severity based on analysis of their causes is essential to priorities safety budgets and safety risk mitigation measures, as required by ICAO regulations.