TrajAgg: Dual-Scale Feature Aggregation with Hybrid Training for Trajectory Similarity Computation in Free Space

With the widespread use of location-tracking technologies, large volumes of trajectory data are continuously generated. Trajectory similarity computation is a core task in trajectory mining with broad applications. However, existing methods still face two key challenges: (1) difficulty in balancing efficiency and representation quality, and (2) reliance on a single training paradigm, which limits the ability to capture both pairwise similarity and batch-level coherence. To address the challenges mentioned above, we propose a trajectory similarity computation framework, named TrajAgg. Specifically, our framework incorporates a novel Aggregation Transformer that efficiently aggregates GPS and grid features through two stages of direct interaction and enhances the expressiveness of the resulting trajectory embeddings. In addition, by integrating two distinct training paradigms, our model captures both fine-grained pairwise relationships and global structural consistency. We further analyze its effectiveness from the perspective of mutual information. Extensive experiments on three publicly available datasets show that TrajAggconsistently outperforms state-of-the-art baselines. Our method achieves average improvements of 15.11%, 16.49%, 10.41% and 40.15% in HR@1 under four distance measures across three datasets, respectively.