A Bayesian network-based model for risk modeling and scenario deduction of collision accidents of inland intelligent ships

Abstract

Safety is an important premise and foundation for the operation of intelligent ships. This paper introduces a novel scenario analysis framework that employs disaster system theory to produce more comprehensive results for identifying scenario elements and calculating collision risks for inland intelligent ships. The framework is utilized to investigate the collision accident risk evolution mechanism. This process is incorporated into Bayesian Network (BN) modeling for ship collisions on inland rivers. By comparing the change in occurrence probability and consequence severity of risk factors for inland ship collision accidents with and without selected intelligent technologies, the collision risk of intelligent ships is quantified. The results indicate that the application of intelligent technologies, such as ship speed optimization and situational awareness, can reduce the occurrence probability of collision accidents and mitigate the severity of their consequences. Moreover, it has been discovered that such intelligent technologies have a greater impact on accidents with severe consequences than those with minor consequences. This research provides a framework for the preliminary safety evaluation of inland intelligent ships. It is of great significance to accelerate the improvement of navigational risk prevention and response-ability of inland intelligent ships in the future.