The “killing zone” revisited: Serial nonlinearities predict general aviation accident rates from pilot total flight hours

•A serial-nonlinear model appears to predict general aviation accident rates from total pilot flight hours despite the inherently noisy nature of such data.•This could serve as an improved independent variable or covariate to control for flight risk during data analysis of aviation accidents.•Applied to FAA data, this class of models implies that GA pilots may face elevated flight risk longer than imagined before leveling off to a baseline rate.

BackgroundIs there a “killing zone” (Craig, 2001)—a range of pilot flight time over which general aviation (GA) pilots are at greatest risk? More broadly, can we predict accident rates, given a pilot's total flight hours (TFH)? These questions interest pilots, aviation policy makers, insurance underwriters, and researchers alike. Most GA research studies implicitly assume that accident rates are linearly related to TFH, but that relation may actually be multiply nonlinear. This work explores the ability of serial nonlinear modeling functions to predict GA accident rates from noisy rate data binned by TFH.MethodTwo sets of National Transportation Safety Board (NTSB)/Federal Aviation Administration (FAA) data were log-transformed, then curve-fit to a gamma-pdf-based function. Despite high rate-noise, this produced weighted goodness-of-fit (Rw2) estimates of .654 and .775 for non-instrument-rated (non-IR) and instrument-rated pilots (IR) respectively.ConclusionSerial-nonlinear models could be useful to directly predict GA accident rates from TFH, and as an independent variable or covariate to control for flight risk during data analysis. Applied to FAA data, these models imply that the “killing zone” may be broader than imagined. Relatively high risk for an individual pilot may extend well beyond the 2000-h mark before leveling off to a baseline rate.

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