Forecasting traffic flow with spatial–temporal convolutional graph attention networks

Traffic flow prediction is crucial for intelligent transportation system, such as traffic management, congestion alleviation and public risk assessment. Recently, attention mechanism and deep neural networks are utilized to capture traffic dependencies. However, two challenges have yet to be well addressed: (i) previous works overlook the global dependencies across different regions; (ii) how to integrate spatial and temporal information aggregation with latent channel-aware semantics. To tackle these issues, we propose a deep spatial–temporal convolutional graph attention network for citywide traffic flow prediction. We first apply the multi-resolution transformer network to capture traffic dependencies among different regions with the encoding of multi-level periodicity. Spatial dependencies are captured by the attentive graph neural networks followed by convolutional networks from local view to global view. We further propose to inject spatial contextual signals into our framework with the designed channel-aware recalibration residual network, which effectively endows model with the capability of mapping spatial–temporal data patterns into different representation subspaces of latent semantics. The extensive experiments on four real-world datasets demonstrate at least 5% performance gain of our framework by comparing with 19 state-of-the-art traffic prediction methods.