Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. It is quite challenging due to the complicated spatiotemporal dependency and essential uncertainty brought about by the dynamic urban traffic conditions. In most of existing methods, the spatial correlation is captured by utilizing graph neural networks (GNNs) throughout a fixed graph based on local spatial proximity. However, urban road conditions are complex and changeable, which leads to the interactions between roads should also be dynamic over time. In addition, the global contextual information of roads are also crucial for accurate forecasting. In this paper, we exploit spatiotemporal correlation of urban traffic flow and construct a dynamic weighted graph by seeking both spatial neighbors and semantic neighbors of road nodes. Multi-head self-attention temporal convolution network is utilized to capture local and long-range temporal dependencies across historical observations. Besides, we propose an adaptive graph gating mechanism to extract selective spatial dependencies within multi-layer stacking and correct information deviations caused by artificially defined spatial correlation. Extensive experiments on real world urban traffic dataset from Didi Chuxing GAIA Initiative have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines. The source code of our model is publicly available at https://github.com/RobinLu1209/STAG-GCN.