A Spatiotemporal Multiscale Graph Convolutional Network for Traffic Flow Prediction

作者：Cao, Shuqin; Wu, Libing^*; Zhang, Rui; Wu, Dan; Cui, Jianqun; Chang, Yanan

来源：IEEE Transactions on Intelligent Transportation Systems, 2024.

DOI：10.1109/TITS.2024.3354802

• Traffic prediction
• spatiotemporal correlations
• multiscale graph
• graph convolutional networks
• cross-scale fusion

Traffic prediction is vital to traffic planning, control, and optimization, which is necessary for intelligent traffic management. Existing methods mostly capture spatiotemporal correlations on a fine-grained traffic graph, which cannot make full use of cluster information in coarse-grained traffic graph. However, the flow variation of clusters in the coarse-grained traffic graph is more stable compared with nodes in the fine-grained traffic graph. And the flow variation of a fine-grained node is generally consistent with the trend of the cluster to which the node belongs. Thus information in the coarse-grained traffic graph can guide feature learning in the fine-grained traffic graph. To this end, we propose a Spatiotemporal Multiscale Graph Convolutional Network (SMGCN) that explores spatiotemporal correlations on a multiscale graph. Specifically, given a fine-grained traffic graph, we first generate a coarse-grained traffic graph by graph clustering, and extract spatiotemporal correlations on both fine-grained and coarse-grained traffic graphs. Then we propose a cross-scale fusion (CF) to implement information diffusion between the fine-grained and coarse-grained traffic graphs. Moreover, we employ an adaptive dynamic graph convolution network to mine both static and dynamic spatial features. We evaluate SMGCN on real-world datasets and obtain a 1.18% -3.32% improvement over state-of-the-arts.

• 单位
  南京理工大学; 武汉大学