The invention discloses a traffic flow prediction method of a graph convolution network based on double-wavelet transformation, and the method comprises the steps: firstly carrying out the analysis of an original traffic flow signal through employing two different wavelet basis functions, overcoming some limitations of single-wavelet transformation, and relieving the boundary effect problem of wavelet transformation; and compared with the traditional dynamic graph convolution, the method of embedding the predefined graph into the dynamic graph has the advantages of higher stability, better convergence effect and higher prediction accuracy. A correlation graph sampling strategy is used, and the graph convolution calculation complexity is reduced on the premise that the influence on the model prediction effect is not large. Moreover, prediction result loss, prediction trend component loss and L2 regularization terms of model parameters are adopted as overall training loss, it is ensured that when the model is trained, prediction errors are reduced, the overfitting problem can be effectively avoided, the model can keep good performance when facing new data, and the prediction accuracy of the model is improved. Therefore, a more reliable prediction capability is realized.
本发明公开一种基于双小波变换的图卷积网络的交通流预测方法,该方法首先通过使用两种不同的小波基函数对原始交通流信号进行分析,克服单小波变换的一些局限性,缓解小波变换的边界效应问题。并且使用动态图中嵌入预定义图的方式,相比于传统的动态图卷积来说,具有更强的稳定性,收敛效果更好,预测精确度更高。使用相关图采样策略,在对模型预测效果影响不大的前提下,降低图卷积计算复杂度。而且,采用预测结果损失、预测趋势分量损失和模型参数的L2正则化项作为整体的训练损失,确保模型在进行训练时,不仅关注于减少预测误差,还能有效避免过拟合问题,也使模型在面对新数据时保持良好的表现,从而实现更为可靠的预测能力。
Traffic flow prediction method of graph convolutional network based on double-wavelet transform
一种基于双小波变换的图卷积网络的交通流预测方法
13.05.2025
Patent
Elektronische Ressource
Chinesisch
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