Urban Traffic Flow Forecasting Based on Spatial-Temporal Graph Contrastive Learning

As the urbanization accelerates, traffic congestion and accidents problems are more serious. Accurate traffic flow forecasting is essential for urban traffic management and optimization. Recently, graph neural networks (GNNs) based forecasting methods have obtained superior results. However, there are still two challenges: 1) The majority of traffic flow forecasting methods are rooted in supervised learning and lack considering data noise, while fewer studies focusing on contrastive learning. 2) Urban traffic networks exhibit not only local spatial dependencies but also global spatial correlations. To tackle these challenges, we propose a novel Urban Spatial-Temporal Graph Contrastive Learning framework(USTGCL). Specifically, we first perform augmentation on the traffic spatial-temporal data through topology and feature-level strategies to learn local and global information. Following a simple encoder, we apply contrastive learning auxiliary task in the high-level representations to jointly learn similarities and differences within the traffic data. Extensive experimental results demonstrate that our model outperforms the state-of-the-art baselines on two urban datasets, achieving an improvement of approximately 4.67%.