Spatio-Temporal Contrastive Learning-Based Adaptive Graph Augmentation for Traffic Flow Prediction

Traffic flow prediction plays a pivotal role in intelligent transportation systems. While previous efforts have made significant advances in modeling spatio-temporal dependencies, the traffic data collected by sensors in real-world scenarios is inherently limited, and the scarcity of data impedes model optimization. Moreover, it is difficult for existing models to capture the dynamic properties caused by changes in traffic patterns because they often use shared parameterization to model the time correlations of all time periods. To address these challenges, we propose a Spatio-Temporal Contrastive Learning-based Adaptive Graph Augmentation (STCL-AGA) framework for traffic flow prediction, which captures and adapts to evolving traffic patterns by employing dynamic graph adjustments and flow data masking. Specifically, our approach leverages spatio-temporal correlations captured through convolution operations. It improves adaptability through adaptive graph augmentation, which dynamically adjusts the graph structure and incorporates nodes flow masking based on traffic conditions. This approach achieves accurate traffic predictions by effectively integrating temporal and spatial data transformations. Experiments on five benchmark datasets demonstrate that STCL-AGA consistently outperforms various state-of-the-art baselines.