Work

Identification of key risk-influencing factors in merchant ship collision occurrence using a data-driven bayesian network with emphasis on ship-related factors

Year: 2026

Type: article

Abstract: Identifying key risk-influencing factors (RIFs) for collision occurrence is essential for preventing accidents. While most studies focus on accident severity, few address occurrence. Accident data alone are insufficient for understanding the occurrence, which requires comparing vessels involved in accidents with those that were not—necessitating exposure data. Automatic Identification System (AIS) data are often used for this purpose but limit the analysis to short-term or regional scopes. To overcome these limitations, this study constructed a ship × year-based database using long-term, global data from world fleet and maritime casualty databases. This study used a tree-augmented naïve Bayes-based Bayesian network (BN) to identify RIFs contributing to collision occurrence, focusing on ship-related RIFs. The relative importance of each factor was evaluated by introducing evidence into the BN and calculating the posterior probability of collision. The results reveal that the period and built year have a greater influence on collision occurrence than age. Other significant RIFs included the gross tonnage, draught, speed, and deadweight tonnage. Incorporating these RIFs into conventional risk assessments can enhance objectivity, reducing reliance on subjective expert judgement and strengthening decision-making in safety management.

Source: Applied Ocean Research

DOI: 10.1016/j.apor.2026.105199

Author: Tomohiro Yuzui

Institutions: National Maritime Research Institute, National Institute of Maritime, Port and Aviation Technology

Language: English

Topic: Maritime Navigation and Safety

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,950

Funder: Japan Society for the Promotion of Science