Multi-dynamic residual graph convolutional network with global feature enhancement for traffic flow prediction

The key to achieving an accurate and reliable traffic flow prediction lies in modeling the complex and dynamic correlations among sensors. However, existing studies ignore the fact that such correlations are influenced by multiple dynamic factors and the original sequence features of the traffic data, which limits the deep modeling of such correlations and leads to a biased understanding of such correlations. The extraction strategies for global features are less developed, which has degraded the reliability of the predictions. In this study, a novel multi-dynamic residual graph convolutional network with global feature enhancement is proposed to solve these problems and achieve an accurate and reliable traffic flow prediction. First, multiple graph generators are proposed, which fully preserve the original sequence features of the traffic data and enable layered depth-wise modeling of the dynamic correlations among sensors through a residual mechanism. Second, an output module is proposed to explore extraction strategies for global features, by employing a residual mechanism and parameter sharing strategy to maintain the consistency of the global features. Finally, a new layered network architecture is proposed, which not only leverages the advantages of both static and dynamic graphs, but also captures the spatiotemporal dependencies among sensors. The superiority of the proposed model has been verified through extensive experiments on two real-world datasets.