Ship navigation behavior pattern mining: Representations and intelligent methods

Maritime transportation is a core pillar of global trade. Mining meaningful behavioral patterns from ship trajectories is essential for characterizing ship movement regularities, supporting maritime traffic management, and improving maritime situational awareness. Although substantial progress has been made in anomaly detection, trajectory prediction, and scenario-specific behavior recognition, publicly available labeled datasets remain limited by incomplete labeling schemes, and ship behavior modeling still lacks a unified framework. To clarify these gaps, this review systematically examines data sources, labeled datasets, behavior modeling, mining methods, and practical applications related to ship navigation behavior pattern mining. It further proposes a three-layer behavior modeling framework that organizes ship behavior into physical behavior, interactive behavior, and semantic behavior. This framework connects low-level motion states, spatiotemporal interactions with the maritime environment and other ships, and high-level semantic interpretation, thereby providing a structured basis for understanding behavioral patterns in ship trajectories. Building on this review, we summarize the major challenges and future research directions in the field. This study provides a reference for subsequent research and aims to promote the development of ship navigation behavior pattern mining, particularly by extending spatial data mining methods for static data toward spatiotemporal pattern mining of dynamic streaming data from moving objects.