Adaptive Spatial-Temporal Fusion Graph Convolutional Networks for Traffic Flow Forecasting

Abstract

Traffic flow forecasting is a significant issue in the field of transportation. Early works model temporal dependencies and spatial correlations, respectively. Recently, some models are proposed to capture spatial-temporal dependencies simultaneously. However, these models have three defects. Firstly, they only use the information of road network structure to construct graph structure. It may not accurately reflect the spatial-temporal correlations among nodes. Secondly, only the correlations among nodes adjacent in time or space are considered in each graph convolutional layer. Finally, it's challenging for them to describe that future traffic flow is influenced by different scale spatial-temporal information. In this paper, we propose a model called Adaptive Spatial-Temporal Fusion Graph Convolutional Networks to address these problems. Firstly, the model can find cross-time, cross-space correlations among nodes to adjust spatial-temporal graph structure by a learnable adaptive matrix. Secondly, it can help nodes attain a larger spatiotemporal receptive field through constructing spatial-temporal graphs of different time spans. At last, the results of various spatial-temporal scale graph convolutional layers are fused to produce node embedding for prediction. It helps find the different spatial-temporal ranges' influence for various nodes. Experiments are conducted on real-world traffic datasets, and results show that our model outperforms the state-of-the-art baselines.