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.
翻译:轨迹相似性措施作为轨迹数据库的查询前置,使它们成为确定查询结果的关键角色。它们也对查询效率有重大影响。 理想的措施应该有能力在非常短的时间内准确评估任何两个轨迹之间的相似性。 为此,我们提议了一种以学习为对比的以学习为基础的轨迹模型方法,名为TrajCL。 我们提出了四种轨迹增强方法和一种新型的双功能自关注轨道主干编码器。 由此产生的模型可以共同学习轨迹的空间和结构模式。 我们的模型并不涉及任何经常性结构,因此效率很高。 此外,我们预先训练的骨干编码器可以用最低限度的监督数据微调调整到其他计算成本昂贵的措施。 实验结果显示, TrajCL 与最新水平的轨迹类似测量方法一样, 持续且显著地更加精确。 经过微调, 即, TrajCL甚至可以超越州级轨迹查询的精确度, 直至56 % 。