Global-Local Spatiotemporal Perception Model for Traffic Flow Prediction

Traffic flow prediction is crucial for intelligent transportation systems; however, the existing methods cannot accurately capture the temporal and spatial correlation of traffic data. To further explore the complex spatiotemporal correlation of road networks and improve prediction performance, a spatiotemporal graph attention network GL-STAGGN model considering global-local spatiotemporal perception is proposed. First, the spatiotemporal heterogeneity of traffic flow is represented by embedding the spatiotemporal location of the input data to enhance the feature representation of spatiotemporal data; subsequently, global-local time-aware multi-head self-attention synchronization is used to mine the global and local spatiotemporal dynamic correlation. Second, a graph attention network and a dynamic graph convolutional network based on the attention mechanism are introduced to aggregate local node features and dynamically adjust the spatial correlation intensity for capturing the internal correlation between global and local spatial correlations in depth. Finally, the GL-STAGGN model is constructed using an encoder-decoder architecture to fuse the spatiotemporal components. Experimental results on real-world highway traffic datasets, PEMS04 and PEMS08, show that compared with the advanced method DSTAGNN, which does not consider the global-local spatiotemporal relationship and spatial heterogeneity, the average Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) decreased by 2.8%, 2.3%, and 3.3%, respectively. Furthermore, GL-STAGGN performs better than most existing baseline models in terms of supporting intelligent transportation systems.