Accident Data-Driven Consequence Analysis in Maritime Industries

Abstract

Maritime accidents are significant obstacles to the development of shipping industries. Their consequences are another important issue because they often involve significant economic losses and human casualties. Accident consequences do not occur randomly, but are triggered by a series of influential factors. To determine the critical factors contributing to accident consequences, a data-driven research framework is proposed. Firstly, 198 maritime accident investigation reports from the Marine Accident Investigation Branch (MAIB) and Australian Transport Safety Bureau (ATSB) are collected to build a database. Secondly, relevant influential factors are identified based on a literature review. Thirdly, a TAN (Tree Augmented Network)-based BN (Bayesian network) model is developed. Fourthly, a model validation process, including a comparative analysis, Kappa test, and scenario analysis are performed. The five critical factors are determined as accident type, ship type, ship age, ship length and gross tonnage. Valuable implications are generated through this research framework and can be a valuable reference for the safety management of concerned parties. In addition, the TAN model can be a predictor for developing mitigation measures to minimize accident consequences.

Keywords

• maritime accident
• accident consequences
• TAN
• maritime safety