SDSCNN: A Hybrid Model Integrating Static and Dynamic Spatial Correlation Neural Network for Traffic Prediction

Abstract

Traffic flow prediction is of great significance for traffic control, and it has been challenging for capturing the complex spatial-temporal correlation. However, most existing prediction methods only consider the spatial adjacency of the nodes (i.e., static spatial correlation), lacking sufficient analysis of non-stationary traffic conditions (i.e., dynamic spatial correlation). The combination of static spatial correlation and dynamic spatial correlation enables the model to comprehensively analyze the feature of traffic flow at each moment and improve the mining capability. To address this problem, we use the multi-head self-attention mechanism to establish a hybrid model integrating static and dynamic spatial correlation neural network (SDSCNN) for traffic flow prediction. Specifically, we first construct static adjacency matrix and dynamic adjacency matrix according to different methods. These two matrices are simultaneously input into Graph Attention Network for analysis. The two outputs are integrated by the sum operation. Then the fused static and dynamic spatial features are fed into the multi-head self-attention layer to analyze the temporal correlation. Also, multi-layer SDSCNNs are stacked to further analyze the dynamic correlations between road sections, as well as to improve the model's multi-step prediction capability. Finally, Multi-layer Perceptron is used to output the prediction results. Extensive experiments are conducted using the datasets PEMS04, PEMS08, and METR-LA. And the results demonstrate that our model shows a good prediction performance.