A Graph Convolutional Stacked Temporal Attention Neural Network for Traffic Flow Forecasting

As the foundation of route planning and applications for intelligent transportation systems, accurate spatiotemporal traffic forecasting plays an essential role in improving both road utilization and traffic safety. Recently, the graph convolution network (GCN), recurrent neural network (RNN) and many other deep-learning based methods have been adopted for traffic flow forecasting and performed much better than the conventional statistical approaches. However, some node information may be lost during the propagation in graph convolutional layers, and the existing methods are insufficient to model the temporal dependencies especially for long-range sequences. In order to address these deficiencies, we innovatively come up with a graph convolutional stacked temporal attention neural network (GSTA), which can simultaneously extract the spatial and temporal features to forecast the traffic flow with higher accuracy. Specifically, our proposed framework uses a mix-hop GCN to better capture the spatial dependencies by preserving more useful information compared with the traditional GCN. Moreover, to identify the relations among traffic flow data over different time steps, we adopt an attention mechanism and introduce the temporal feature through embedding technology to capture the temporal regularity. We evaluate the proposed GSTA on two real-world traffic datasets, and the experimental results demonstrate the performance of our proposed model is significantly superior to several existing methods.