Evaluation of preconditions affecting symptomatic human error in general aviation and air carrier aviation accidents

Human error remains the leading cause of accidents in the aviation industry, as technological reliability and system safety have undergone significant improvements. Improved methods are required to model the events resulting in human error incidents. In this study, aviation accident data are evaluated to model the association between the latent and symptomatic causal factors resulting in aviation mishaps. We comparatively analyze National Transportation Safety Board accident data for general aviation and air carrier pilots in order to evaluate potential causal factors. The demonstrated methodology leverages previous work in causal relationships by using multiple-variable logistic regression to model the relationships among latent causal factors, symptomatic causal factors, and accident severity. The Human Factors Analysis and Classification System is applied to define a framework intended to identify focal areas for the safety community to mitigate similar future system failures. The results demonstrate an effective methodology for evaluating the quantitative relationships between symptomatic and latent causal factors, which are not readily apparent based solely on occurrence rates. Furthermore, the results also clarify the differences in causal factors between the selected general aviation and air carrier pilot operations. The usefulness of the framework, transferability to other domains, and possibilities for future research are discussed.