  Trajectory similarity measures act as query predicates in trajectory
databases, making them the key player in determining the query results. They
also have a heavy impact on the query efficiency. An ideal measure should have
the capability to accurately evaluate the similarity between any two
trajectories in a very short amount of time. Towards this aim, we propose a
contrastive learning-based trajectory modeling method named TrajCL. We present
four trajectory augmentation methods and a novel dual-feature
self-attention-based trajectory backbone encoder. The resultant model can
jointly learn both the spatial and the structural patterns of trajectories. Our
model does not involve any recurrent structures and thus has a high efficiency.
Besides, our pre-trained backbone encoder can be fine-tuned towards other
computationally expensive measures with minimal supervision data. Experimental
results show that TrajCL is consistently and significantly more accurate than
the state-of-the-art trajectory similarity measures. After fine-tuning, i.e.,
to serve as an estimator for heuristic measures, TrajCL can even outperform the
state-of-the-art supervised method by up to 56% in the accuracy for processing
trajectory similarity queries.
