An efficient and interpretable clustering-based framework for large-scale maritime traffic pattern recognition

Understanding maritime navigational behaviors is fundamental for safe and intelligent shipping systems. Existing trajectory clustering approaches often suffer from two main limitations: computational inefficiency caused by complex similarity measures and repeated parameter tuning, and poor interpretability due to clusters that lack characterization through operational features and interaction patterns. To address these issues, this study proposes an efficient and interpretable framework for large-scale maritime traffic pattern recognition. First, the Hierarchical Jaccard on Geohash (HiJaG) method is developed for streamlined similarity measurement, which incorporates hierarchical spatial gridding and set-based comparison to achieve scale adaptivity and expedites the computation of the synthetic similarity matrix. Then, the Similarity Graph-based Leiden (SG-Leiden) clustering method is introduced to improve clustering robustness and eliminate cumbersome parameter adjustment by transforming the similarity matrix into a graph for Leiden algorithm. Finally, a pattern interpretation step profiles each cluster via spatial coverage, movement regularity, and behavioral properties, and examines encounter-driven interaction patterns. A case study using Automatic Identification System (AIS) and Traffic Separation Scheme (TSS) data from the Gulf of Finland was conducted to validate the proposed framework. Compared with baseline methods, it achieves superior accuracy and efficiency, with computation time reduced by an order of magnitude. The interpretation results further reveal distinct pattern characteristics. These findings highlight the practical value of this work in supporting enhanced navigation safety and maritime traffic management. • Streamlined similarity matrix computation for AIS trajectories via a novel method. • Similarity graph transformation enables trajectory clustering with Leiden algorithm. • Interpretable pattern discovery through cluster profiling and encounter analysis. • The proposed approach improves efficiency by an order of magnitude. • The approach outperforms six baselines with marked performance gains.