A traffic flow forecasting model with embedded sparse dynamic graph convolution and multi-head attention LSTM

Abstract

To address the limitations of graph convolution-based traffic flow forecasting models, in which predefined static graphs are unable to adapt to the dynamic evolution of road networks and conventional LSTM models fail to finely characterize temporal dynamics, this study proposes an ESDG-ALSTM traffic flow forecasting model, namely an Embedded Sparse Dynamic Graph Convolutional LSTM model with Multi-Head Attention. First, a sparse dynamic graph convolution module is developed to integrate the physical topology and implicit semantic features of traffic flow data. Combined with a Top-K strategy, the adjacency matrix is dynamically reconstructed to enable adaptive capture of time-varying spatial dependencies while effectively suppressing long-tail noise. Next, an improved LSTM unit enhanced by multi-head attention is designed to model abrupt events and multimodal evolution patterns in traffic flow. Through multi-subspace projection and dynamic weight allocation, the hidden-state update process of the LSTM unit is restructured, thereby enabling deep extraction of traffic temporal features. Finally, a deeply coupled spatiotemporal feature network is constructed, in which graph convolution is further employed to refine the output states of the LSTM gates, achieving deep coupling and synchronized extraction of spatial and temporal features. Experimental results on the public PEMS04 and PEMS08 datasets demonstrate that the proposed model significantly improves forecasting accuracy, confirming that ESDG-ALSTM is more sensitive to abrupt events and multimodal evolution patterns and can effectively enhance prediction performance in complex traffic flow scenarios.