Integrating ship traffic conflicts into navigation risk modelling for safety at traffic-intensive waters
Year: 2025
Type: article
Abstract: The rapid expansion of the shipping industry has significantly reinforced the necessity for bespoke navigation risk assessments against different navigational scenarios such as high-risk waters of intensive traffic. This study aims to develop a novel model that pioneers the integration of ship traffic conflict into navigation risk analysis for safety at sea. Firstly, a ship traffic conflict model that can simultaneously take into account the probability and potential consequences of ship collisions is developed to effectively analyse the ship traffic conflict in an investigated high-risk water and quantify it by a numerical indicator. Secondly, an Intuitionistic Fuzzy Multi-Attribute Boundary Approximation Area Comparison (IF-MABAC) method is used to enable a comprehensive evaluation of navigation risks and the identification of key risk factors by merging objective data with expert opinions. Finally, this model is applied to analyse five critical areas in the Bohai Sea, and quantify their respective navigation risks. The findings from this study not only highlight the significance of integrating ship traffic conflicts into navigation risk assessments but also demonstrate regional variations in risk factors to guide rational bespoke safety measure development in each region. These insights inform targeted risk control strategies and offer crucial guidance for improved maritime safety in high-risk areas. Compared to the state-of-the-art studies in the field, this study makes new contributions to tailoring risk assessments to specific environmental and traffic conditions and hence improve the accuracy and effectiveness of navigation risk management.
Source: Ocean Engineering
Authors: Yao Sun, Jiaxuan Yang, Xinjian Wang, Ken Qin, Zaili Yang
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W4414005804