Improved identification of maritime risk-influencing factors using AIS data in regression analysis

Understanding risk-influencing factors (RIFs) associated with the occurrence of maritime accidents is important to prevent their future occurrence, identify high-risk ships, and properly influence policy. However, current methods often suffer from selection bias or do not account for variations in ship exposure, leading to biased or incomplete assessments. This study addresses these gaps by incorporating AIS-derived activity metrics as offset variables in regression analysis. This transformation of the dependent variable leads to the analysis of accident rates. The method is applied to ship losses of command (i.e., loss of propulsion, loss of electrical power, or loss of directional control / steering) by cargo ships in Norwegian waters from 2017 to 2021. Significant variables influencing the rate of loss of command include ship’s flag state, ship manager domicile, number of inspection deficiencies, propulsion redundancy, and the use of a single fuel onboard. Inspection deficiencies and sailing with a flag of convenience are associated with increased rates. Sailing with a Norwegian ship manager, propulsion redundancy, and a single fuel type onboard are associated with decreased rates. Incorporating measures of ship activity as exposure significantly improves the overall model fit, leading to better identification of RIFs associated with the occurrence of ship losses of command. Sensitivity analyses using sailed distance as exposure and the Cox proportional hazards model demonstrate overall robustness. The results are beneficial for identifying high-risk ships from the perspective of vessel traffic management and can be used for decision-making. The method has promising potential for the future analysis of RIFs associated with other types of maritime accidents.