Overcoming Clustering Bottlenecks in Ship Trajectory Pattern Recognition

Understanding ship trajectory patterns performs an essential role in enhancing navigation safety, optimizing shipping management, and facilitating the development of intelligent navigation systems. Driven by the widespread adoption of Automatic Identification System (AIS) and the development of data mining technologies, ship trajectory pattern recognition has witnessed a growing academic interest in intelligent maritime transportation. As one of the key techniques in this research domain, clustering holds a critical position due to its unsupervised nature, flexibility, and robust pattern discovery capabilities. In contrast to prevailing deep learning methods, which predominantly focus on complex microscopic pattern analysis and dynamic real-time applications, clustering is widely employed in static analysis of historical data to uncover long-term regularities. It functions as the groundwork for a preliminary understanding of macroscopic Maritime Situational Awareness (MSA). However, based on a comprehensive literature review, the bottlenecks of current clustering-based methods lie in: (1) Inefficient similarity measurement, leading to high computational costs and scalability issues; (2) Cumbersome clustering parameter tuning process, involving recursive search, statistical analysis, and manual adjustments; (3) Suboptimal parameter selection, hindering accurate capture of trajectory patterns. To address these challenges, this research introduces a Geohash-based multi-precision gridding strategy to develop a novel clustering-based method for rapid and precise ship trajectory pattern recognition.