Efficiently capturing the complex spatiotemporal representations from large-scale unlabeled traffic data remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data, which not only perturb the data in terms of graph structure and temporal characteristics, but also employ a learning-based dynamic graph view generator for adaptive augmentation. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.

翻译：摘要：从大规模无标注交通数据中高效提取复杂时空表征仍是一项挑战性任务。针对这一难题，本文采用先进的对比学习方法，提出了一种新颖的时空同步上下文对比学习（STS-CCL）模型。首先，我们设计了面向时空图数据的基础增强与强增强方法，该方法不仅在图结构和时间特征维度对数据进行扰动，还引入基于学习的动态图视图生成器实现自适应增强。其次，我们构建了时空同步对比模块（STS-CM），该模块可同步捕获优质的时空依赖关系并实现图级对比。为在负样本筛选中进一步区分节点个体，基于语义特征与空间异质性设计了一种语义上下文对比方法，实现了融合负样本筛选的节点级对比学习。最后，我们提出硬互视图对比训练方案，并将经典对比损失扩展为集成目标函数，从而获得更优性能。大量实验与评估表明，基于STS-CCL对比学习模型构建的预测器，其性能优于现有交通预测基准模型。所提出的STS-CCL特别适合仅含少量标注数据的大规模数据集，以及面临数据稀缺问题的其他时空任务。