Qualitative Analysis of Loss of Control Aircraft Accidents Using Text Mining Techniques

<p>The purpose of this study was to analyze the narrative National Transportation Safety Board (NTSB) accident cause descriptions for general aviation (GA) Loss of Control (LOC) accident reports by the different certification categories: Federal Aviation Regulations Part 23 (Part 23), Civil Air Regulations 3 (CAR 3), Light Sport Aircraft (LSA), and Experimental-Amateur Built (E-AB). Airworthiness certification categories represent a wide diversity of government oversight. Part 23 rules have evolved from the initial set of simpler design standards and have progressed into a comprehensive and strict set of rules to address the safety issues of the more complex airplanes within the category. E-AB airplanes have the least amount of government oversight and are the fastest-growing segment. The LSA category is a more recent certification category that utilizes consensus standards in the approval process. CAR 3 airplanes were designed and manufactured under simpler rules, but modifying these airplanes has become lengthy and expensive. The qualitative analysis involved the use of text mining techniques for the analysis of the narrative cause descriptions contained within the accident reports. The text mining analysis of the narrative causes of LOC accidents indicated that only the CAR 3 category airplanes had clusters of words associated with visual flight into instrument meteorological conditions. CAR 3 airplanes were designed and manufactured prior to the 1960s, and in most cases have not been retrofitted to take advantage of newer technologies that could help prevent LOC accidents. According to the literature, government oversight could have become an obstacle in the implementation of safety enhancing equipment that could reduce LOC accidents.</p>