Accident analysis models are crucial tools for understanding and preventing accidents in the maritime industry. Despite the advances in ship technology and regulatory frameworks, human factors remain a leading cause of marine accidents. The complexity of human behavior, influenced by social, technical, and psychological aspects, makes accident analysis challenging. Various methods are used to analyze accidents, but no single approach is universally chosen for use as the most effective. Traditional methods often emphasize human errors, technical failures, and mechanical breakdowns. However, hybrid models, which combine different approaches, are increasingly recognized for providing more accurate predictions by addressing multiple causal factors. In this study, a dynamic hybrid model based on the Human Factors Analysis and Classification System (HFACS) and Bayesian Networks is proposed to predict and estimate accident risks in narrow waterways. The model utilizes past accident data and expert judgment to assess the potential risks ships encounter when navigating these confined areas. Uniquely, this approach enables the prediction of accident probabilities under varying operational conditions, offering practical applications such as real-time risk estimation for vessels before entering the Istanbul Strait. By offering real-time insights, the proposed model supports traffic operators in implementing preventive measures before ships enter high-risk zones. The results of this study can serve as a decision-support system not only for VTS operators, shipmasters, and company representatives but also for national and international stakeholders in the maritime industry, aiding in both accident probability prediction and the development of preventive measures.
