Spatio-Temporal Mamba Dynamic Graph Convolutional Recurrent Network for Traffic Prediction

In intelligent transportation systems (ITS), traffic prediction has become a core issue in the field of artificial intelligence. Accurate traffic prediction is crucial for reducing congestion, improving travel efficiency, and enhancing traffic safety. However, the complex and dynamically changing spatiotemporal dependencies in traffic networks make the prediction task highly challenging. Most existing methods rely on selfattention mechanisms to capture these spatio-temporal dependencies, which often result in high computational complexity. Furthermore, although traffic systems can be modeled as graph structures, graph convolutional network (GCN), which effectively capture correlations between nodes, typically depend on predefined or adaptive adjacency matrices. These approaches struggle to cope with the highly dynamic nature of real-world traffic systems. To address these issues, we propose a spatio-temporal Mamba dynamic graph convolutional recurrent neural network (STMAGRN). This model first leverages the Mamba framework to extract spatio-temporal features from traffic sequences. The Mamba model, through its unique selective state space mechanism and linear time complexity, achieves efficient sequence processing. Next, we design a spatio-temporal memory module (STM), which identifies and extracts traffic pattern features most similar to the input data, and learns intrinsic representative traffic patterns from the data associated with each node. These patterns are then used to dynamically generate graph structures, providing GCN with real-time updated graph data. Finally, we combine GCN with recurrent neural networks (RNNs) to extract both spatial and temporal features simultaneously. We conducted extensive experiments on six real-world datasets, and the results demonstrate that STMAGRN significantly outperforms state-of-the-art methods in traffic prediction tasks.