CASTNet: Convolution Augmented Graph Sampling Transformer Network for Traffic Flow Forecasting

Zixuan Chen, Shengjie Zhao, Jin Zeng, Shilong Dong, Geyunqian Zu

Abstract: Accurate and efficient traffic flow forecasting plays an essential role in urban management which is conductive to traffic safety and travel experience. Despite the tremendous progress of attention-based traffic flow forecasting methods, complicated spatial and temporal correlations of traffic data pose difficulty for the network in simultaneously capturing the long-range contexts and the local feature details, leading to limited performance. To this end, we propose Convolution Augmented Graph Sampling Transformer Network (CASTNet) for accurate traffic flow prediction. Specifically, we design a hybrid graph transformer module to utilize the graph convolutions to perceive local structural details, which are incorporated into the transformer to augment the attention computation for more accurate global dependency modeling. Moreover, we develop a sampling-based attention layer based on the approximated sampling strategy to further optimize the graph structure, which is implemented with linear complexity. Finally, the spatial features from the graph transformer module are fused with the temporal features from the sample convolution and interaction network. Experimental results on three real-world traffic flow datasets demonstrate the superiority of the proposed CASTNet over existing schemes.