Traffic Prediction Method Integrating Graph Wavelet and Attention Mechanism

Traffic Prediction Method Integrating Graph Wavelet and Attention Mechanism

Blog Article

Traffic predicting is a critical component of modern intelligent transportation systems for traffic management and control.However, the traffic flow is complex.On one hand, the urban road structure is highly correlative, and there often exists a nonlinear structural dependence between different roads.

On the other hand, traffic flow data often change dynamically over time.In recent years, many studies have tried to use deep learning methods to extract Star Wars complex structural features in traffic flow.However, the process of local feature extraction still lacks flexibility, and ignores the dynamic variability as well as the correlation of spatio-temporal features.

To this end, this paper proposes a new traffic prediction method integrating graph wavelet and attention mechanism.This method uses wavelet transform and an adaptive matrix to extract local and global spatial features of traffic flow respectively, and combines the improved recurrent neural network to extract local temporal characteristic information.Meanwhile, the attention mechanism is adopted in this method to Wall Hook capture the temporal and spatial dynamic variability.

Then this method applies a spatio-temporal feature fusion mechanism to fusing local and global temporal and spatial features.Experimental results show that this method can extract spatial and temporal features of real traffic datasets well, and it outperforms the existing methods.