Adaptive dynamic spatial-temporal graph convolutional neural network for traffic flow prediction

Accurate and efficient traffic flow prediction is essential for developing smart cities. Traffic flow data exhibits complex spatio-temporal dependencies, and the weights between nodes may change dynamically due to travel patterns and node attributes. However, existing prediction models primarily rely on static adjacency matrices and complex time series models, which limit model performance. We propose an innovative traffic flow prediction method, the Adaptive Dynamic Spatio-temporal Graph Convolutional Network (ADSTGCN), to address this issue. Specifically, we incorporate a multi-head attention mechanism and an adaptive dynamic adjacency matrix to construct two dynamic spatio-temporal extraction modules, which are integrated with Graph Convolutional Networks (GCN) to overcome the limitation of static adjacency matrices in capturing dynamic spatio-temporal correlations between nodes. At the temporal dimension extraction level, we integrate the Mamba to model long-term time series in traffic flow data, effectively extracting relevant temporal information. Extensive comparative experiments are conducted on four real-world public transportation datasets. The results demonstrate that our model achieves the highest prediction accuracy compared to other baseline models, showcasing its significant potential in traffic flow prediction.