To assess road traffic safety risk in civil aviation airports and develop effective accident prevention measures, this study proposed a risk assessment method based on accident tree and Bayesian network for airport aircraft activity areas. It identified influencing factors in the aircraft activity area from the perspectives of person-vehicle-road-environment-management and analyzed their relationships. The Bayesian network was utilized to determine initial probabilities for each influencing factor. Findings indicated a relatively high overall safety level in the airport's road traffic system. Accident trees were employed to qualitatively and quantitatively analyze common human-vehicle accident patterns. The initial probabilities obtained from the Bayesian network served as basic event probabilities in the accident tree to determine the occurrence probability of the top event. Taking a 4F airport in China as an example, accident cause analysis identified five important risk sources in human-vehicle accidents, including blind spots for special vehicles, illegal driving by drivers, pedestrians violating regulations, passengers entering restricted areas, and blind spots at intersections. Corresponding safety management measures were formulated. The study concluded that the integration of Bayesian networks and accident trees effectively determines accident probabilities and offers specific solutions, thus playing a crucial role in enhancing road traffic safety management within aviation airports.
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