SMGNN: Semantic Multi-Connected Graph Neural Network for Traffic Flow Prediction

Traffic flow prediction, as one of the problems of spatial correlation analysis of time series, has been extensively studied. The extraction and fusion of effective spatio-temporal features are crucial for achieving high-precision traffic flow prediction. Traditionally, the adjacency graph designed based on the neighboring nodes of real-world road networks has been indispensable for learning spatial features. However, this single connected component graph structure is prone to the phenomenon of over-smoothing, leading to homogenization of the learned spatial feature. Addressing this challenge, this paper proposes a novel Semantic Multi-connected Graph Neural Network (SMGNN) aimed at mitigating the homogeneity of spatial features and effectively modeling spatio-temporal interactions. Firstly, considering the existence of several nodes in large-scale road networks with similar traffic flow variation patterns, we semantically connect these nodes to construct multi-connected semantic spatial graphs (MSSG), replacing the traditionally used neighboring node graph in conventional graph neural networks. Correspondingly, we design a novel graph neural network architecture that cyclically fuses dynamic scale spatio-temporal features from MSSG using an improved Dynamic Spatial Graph Attention (DSGA) module. Secondly, to achieve a more effective representation, we design a Inverted Temporal Attention (ITA) module to supplement static scale temporal features. Furthermore, we introduce a Multi-dimensional and Multi-scale Feature Extraction (MMFE) module to fuse spatio-temporal features at various scales within different receptive fields. Extensive experiments conducted on real-world datasets have verified the effectiveness of our proposed method, significantly outperforming various baseline models.