Development and application of a Bayesian network-based model for systematically reducing safety risks in the commercial air transportation system

• Valuable safety information was retrieved from commercial aviation incidents. • The algorithm of MMHC was used for developing the Bayesian network of CATSSR-BN. • K-fold cross-validation of structure learning validated effectiveness of CATSSR-BN. • Mutual information was adopted for assessing each causal factor’s impact on results. • Resource investment was optimized for safety management in commercial aviation. Aiming to explore the nature of incidents in the commercial air transportation system, multiple causal factor events and result events, and their cause and effect relationships were retrieved from 7,265 incident cases in the Aviation Safety Reporting System (ASRS). The Bayesian network of commercial air transportation system safety risk (CATSSR-BN) was constructed on the basis of the hybrid algorithm of max–min hill-climbing (MMHC). The k-fold cross-validation of structure learning verified the accuracy and effectiveness of the CATSSR-BN. The measure of mutual information between causal factor event and result event was evaluated for determining its importance on safety performance of commercial air transportation system. Ten causal factor events with the highest values of mutual information across different result events were identified. A decision-making model was developed for optimizing resource investment in commercial aviation safety management. As a result, resource investment combination was determined on the combination of five causal factor events, which provided a systematic approach for reducing safety risk in commercial air transportation system and avoiding the occurrence of commercial aviation incidents with a high level of risk.