Towards Efficient and Effective Unimodal Trajectory Representation Learning: A Simple Yet Powerful Approach

Abstract

Trajectory representation learning transforms trajectory data into low-dimensional embeddings for downstream analytics. Although trajectory data inherently contains rich spatiotemporal information that remains to be more deeply explored, recent approaches have increasingly favored integrating external multimodal information rather than deeply mining internal trajectory patterns, resulting in the underutilization of the significant potential inherent in spatiotemporal features. To address these issues, we propose SimTRL, a novel unimodal framework specifically designed for road network-based trajectory data. Specifically, we design a bi-directional mamba-traj encoder that leverages the linear computational efficiency of State Space Models to capture global non-causal topological dependencies, overcoming the limitations of traditional causal models. Furthermore, we introduce a context-aware time encoder mechanism to explicitly model dynamic time embeddings. Extensive experiments conducted on two real-world datasets demonstrate that our model achieves average performance gains of 5.19%, 13.91%, 7.2%, and 2.69% across four tasks, respectively. Compared to multimodal models, our approach achieves an average improvement of 9.06% across all evaluation metrics, with a pre-training speedup of 11.2×. The source code of our model is available at https://github.com/sxgu1/SimTRL.