  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.
