  Trajectory similarity computation is an essential technique for analyzing
moving patterns of spatial data across various applications such as traffic
management, wildlife tracking, and location-based services. Modern methods
often apply deep learning techniques to approximate heuristic metrics but
struggle to learn more robust and generalized representations from the vast
amounts of unlabeled trajectory data. Recent approaches focus on
self-supervised learning methods such as contrastive learning, which have made
significant advancements in trajectory representation learning. However,
contrastive learning-based methods heavily depend on manually pre-defined data
augmentation schemes, limiting the diversity of generated trajectories and
resulting in learning from such variations in 2D Euclidean space, which
prevents capturing high-level semantic variations. To address these
limitations, we propose T-JEPA, a self-supervised trajectory similarity
computation method employing Joint-Embedding Predictive Architecture (JEPA) to
enhance trajectory representation learning. T-JEPA samples and predicts
trajectory information in representation space, enabling the model to infer the
missing components of trajectories at high-level semantics without relying on
domain knowledge or manual effort. Extensive experiments conducted on three
urban trajectory datasets and two Foursquare datasets demonstrate the
effectiveness of T-JEPA in trajectory similarity computation.
