Bidirectional Dynamic Spatiotemporal Graph Recurrent Neural Network for Traffic Flow Forecasting

As a core function of intelligent transportation systems (ITSs), traffic flow forecasting (TFF) is crucial for reducing congestion in urban areas. Although existing studies have made progress in modeling spatiotemporal dependencies within traffic networks, they ignore the deeper correlations between spatiotemporal features and are particularly deficient in capturing long-distance spatial dependencies and complex dynamic temporal features. To address the aforesaid issues, this paper proposes a Bidirectional Dynamic Spatiotemporal Graph Recurrent Neural Network (Bi-DSTGRNN) for TFF. Specifically, we construct a multi-head self-attention-based gated recurrent unit (MSA-GRU) mechanism to model complex dynamic features of the temporal sequence, utilizing multi-head self-attention to uncover global temporal dependencies, and a bidirectional gated recurrent unit (Bi-GRU) is employed to extract long-term trends. we design a GraphSAGE-based dynamic graph neural network (DGraphSAGE), which dynamically aggregates node features and accurately represents both local and global information in road networks. Finally, we develop a multi-layer Chebyshev graph convolutional network to smooth node embeddings and efficiently extract the global dynamic topology of the traffic network. Experimental results on the PEMSD4 and PEMSD8 datasets demonstrate that the Bi-DSTGRNN model outperforms existing baseline models in prediction accuracy.