HFACS Analysis of U.S. General Aviation Accidents Using Bayesian Network

Abstract

Safety is one of the most important factors that affects the sustainable development of the aviation industry. With the increasing robustness of technologies, humans have played a progressively more important causal role in aviation accidents. This paper applies an HFACS-BN model (HFACS: Human Factors Analysis and Classification System; BN: Bayesian Network) to analyze the root causes of aviation accidents. General aviation (GA) accident reports were collected from the U.S. National Transportation Safety Board (NTSB) accident database. The authors encoded the human factors of sample cases based on the HFACS framework and constructed a corresponding BN. From this work, parameter estimation associated with a conditional probability table (CPT) was conducted to determine prior probabilities of contributing factors, and a sensitivity test was conducted to determine the most significant factors. This study provides guidance to the federal government to facilitate risk management in order to reduce fatal general aviation accidents.