Navigation rules are critical for regulating ship behavior, and effective water traffic management requires accurate identification of ships exhibiting abnormal behavior that violates these rules. To address this need, this paper presents an ontology-based method for identifying abnormal ship behavior. First, we analyzed navigation rules (local regulations) to extract key elements. Next, based on this extraction, we built a navigation rule ontology that categorized ship behavior into state behavior (ship behavior at a specific time point) and process behavior (ship behavior in a time interval). We then constructed an abnormal ship behavior ontology, defined using topological relationships and navigation rules. Finally, we constructed inference rules to detect abnormal ship behaviors by using SWRL (Semantic Web Rule Language) and validated the effectiveness of the method with ship instances. The experimental results demonstrate that this method can accurately infer ships’ behaviors that deviate from established navigation rules. This research has significant implications for reducing waterborne traffic accidents, improving navigational safety, and safeguarding maritime traffic.

Large seagoing vessels experience substantial changes in navigational conditions when transitioning from open sea navigation to port connected and restricted waters. Owing to their large dimensions and maneuvering characteristics, their operations are particularly sensitive to local navigational constraints, vessel interactions, and surrounding traffic, resulting in spatially heterogeneous traffic responses. This study proposes a data-driven approach for spatially partitioning large-vessel traffic based on vessel speed dynamics and their influencing mechanisms. First, speed fluctuations and oscillations are identified using a sliding-window approach and continuous wavelet transform. Bayesian networks, Shapley values, and conditional entropy are then integrated to investigate the factors associated with recurrent speed oscillations. Multi-vessel and multi-voyage oscillation events are spatially aggregated to identify key traffic areas and establish direction-specific traffic zones. Historical trajectories are further used to characterize each zone in terms of vessel speed, actual sailing time, and background-traffic equivalent capacity utilization. A case study on the Jiangsu reach of the Yangtze River produces 49 traffic zones, including 25 key traffic zones and 24 normal navigation zones. The resulting zones provide refined spatial inputs for traffic safety analysis, traffic organization, and large-vessel port entry and departure scheduling.