Key points are not available for this paper at this time.
The navigational safety of ships can be impacted by factors such as varying weather conditions, sea states, circadian rhythms and crew physical conditions at different times of the day. Despite numerous studies in the maritime accident field, systematic investigations on the heterogeneous characteristics of accident Risk Influential Factors (RIFs) across different watchkeeping periods remain limited. To address this gap, this study pioneers a multidimensional analysis framework which integrates an Enhanced Multilevel Association Rule Mining (EMARM) algorithm, the Weighted Influence Non-linear Gauge System (WINGS), the Total Adversarial Hasse Diagram Technology (TAHDT), and the Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC). Firstly, the innovative EMARM algorithm is proposed to identify frequent itemsets and enhanced multilevel association rules between RIFs, i.e., at the state level and factor level. Secondly, the WINGS is established in a data-driven manner and employed to elucidate the causality among these RIFs, providing insight into their interactions. Thirdly, the improved TAHDT, a game theory-based method is utilized to establish hierarchical relationships between RIFs, revealing critical interdependencies and causal pathways. Finally, based on the driving forces and dependencies of RIFs, the MICMAC is applied to classify the RIFs and dig their roles within the system. The results indicate a significant heterogeneity in the critical RIFs across different watchkeeping periods, such differences highlight the unique needs of safety management strategies in each period. By clarifying the challenges, the proposed framework offers a new perspective for improving bridge resource management onboard and further contributing to reducing accident occurrences.
Wang et al. (Mon,) studied this question.