Toward empowering maritime traffic services: Vessel trajectory representation learning and community detection

The recent deployment of Automatic Identification System (AIS) enables real-time monitoring of vessel movements, generating massive volumes of navigational data. To extract meaningful behavioral patterns, trajectory clustering has become a key technique for knowledge discovery and decision-making in maritime scenarios, supporting tasks like route optimization, anomaly detection, and situational awareness. However, existing trajectory representation learning methods in maritime scenarios struggle to jointly capture spatial structural and motion dynamics. The non-uniform sampling and varying trajectory lengths also reduce the efficiency of traditional distance-based similarity calculations. Additionally, clustering methods based on pairwise similarity focus on local relationships and struggle to capture global structural relationships among trajectories. To address the aforementioned challenges, this study introduces a generalizable trajectory clustering framework. First, the raw vessel trajectory data are remapped onto two-dimensional grid to construct multi-channel trajectory images, effectively preserving their intrinsic spatiotemporal features. Second, we develop a feature enhance convolutional autoencoder (FE-CAE) that collaboratively models multiple attention modules to integrate spatial structural information and motion semantics in trajectory images, thereby learning discriminative trajectory representations and transforming trajectory similarity in the feature space, enabling fast, efficient and scalable similarity estimation. Finally, we construct trajectory network based on pairwise similarity, in which trajectories with similar navigation behaviors are tightly connected, and apply community detection to identify distinct navigational behavior patterns, thereby establishing a unified paradigm that bridges representation learning and network modeling. Extensive experiments conducted on large-scale AIS datasets from multiple real-world maritime water areas, and demonstrate that the proposed method outperforms widely adopted methods in both clustering efficiency and robustness. Therefore, underscoring the practical significance and wide applicability of the proposed trajectory clustering framework in advancing intelligent vessel traffic service. • A vessel trajectory clustering framework is proposed to identify behavior patterns. • A trajectory representation learning method is introduced by feature autoencoder. • A feature-space similarity-based network is constructed for community detection.