Influence of Personnel Factors on Air Force Fighter Mishaps via Bayesian Regression

Abstract

Aviation safety in the United States (U.S.) military has received growing attention in recent years due to numerous high-profile mishaps. Despite the increased attention, there have been few quantitative analyses of the relationship between pilot attributes and mishap rates. In this study, we use nearly 15 years of U.S. Air Force (USAF) safety and administrative records to investigate the relationship between pilot attributes and fighter aviation mishap rates. First, we present an analysis of flight mishap rates for different mishap classes and fighter aircraft types, referred to as a mission design series (MDS). Second, we quantify pilot attributes and present an analysis of fighter pilot populations across time and MDS. We then model the association between pilot attributes and annual rate of class A, B, and C flight mishaps, which we refer to as high-class mishaps (HCMs), using a Bayesian regression framework. Our results show prior flight experience and key characteristics of an MDS pilot community are associated with the rate of HCMs. Specifically, we find that MDS pilot communities with 10 more flight hours in the past year are, on average, associated with a 5% decrease in HCM rate. Additionally, we find that a 0.1 standard deviation increase in the proportion of pilots who are instructor pilots, distinguished graduates from commissioning source, and graduate degree recipients is associated with a reduction in major aviation mishaps by 2.1%, 2.0%, and 1.3%, respectively. These findings have significant financial implications, given that the cost of a single HCM starts at $50K and can be as high as$200M. In addition to our model results, our efforts to quantify pilot attributes and model the relationship between personnel factors and mishap rates using Bayesian regression and predictive projection for feature selection represent a valuable methodological contribution to aviation accident analysis