CSTRM: Contrastive Self-Supervised Trajectory Representation Model for trajectory similarity computation

Abstract

The trajectory representation model has become a common method for calculating the similarity of trajectories. Existing works have used the encoder–decoder model, which is trained by reconstructing the original trajectory from a noisy trajectory. However, this reconstructive model ignores the point-level differences between these two trajectories and captures only the trajectory-level features. As a result, it achieves low accuracy on ranking tasks. To solve this problem, we propose a novel contrastive model to learn trajectory representations by distinguishing the trajectory-level and point-level differences between trajectories. Furthermore, to solve the lack of training data, we propose a self-supervised approach to augment training pairs of trajectories. Compared with existing models, our model achieves a significant performance improvement on various trajectory similarity tasks.