The invention discloses a traffic flow prediction method based on a decoupling fusion network, and the method comprises the following steps: 1, obtaining traffic flow data, and obtaining time sequence features through a noise enhancement time sequence model; step 2, constructing a bidirectional diffusion feedback space model composed of a forward diffusion feedback space model and a backward diffusion feedback space model; 3, constructing a predefined graph, wherein the predefined graph is output to the bidirectional diffusion feedback space model; 4, constructing a bilinear space-time fusion model, wherein the bilinear space-time fusion model comprises three networks, namely a forward space-time fusion network, a reverse space-time fusion network and an overall fusion network; and step 5, outputting N prediction results from the extracted features through an N-layer bilinear space-time fusion model, splicing all the prediction results, and then obtaining a final prediction result through a feature fusion device. The method can effectively improve the prediction performance and efficiency.
本发明公开了一种基于解耦融合网络的交通流量预测方法,包括如下步骤:步骤1、获取交通流量数据,并通过噪声增强时序模型得到时序特征;步骤2、构建一个由前向和反向扩散反馈空间模型组成的双向扩散反馈空间模型;步骤3、构建预定义图,所述预定义图作为输至双向扩散反馈空间模型;步骤4、构建双线性时空融合模型,所述双线性时空融合模型包括前向时空融合、反向时空融合和整体融合3个网络;步骤5、将提取的特征,通过N层双线性时空融合模型输出N个预测结果,将所有预测结果进行拼接,然后通过特征融合器得到最终的预测结果。该方法能有效提高预测的性能和效率。
Traffic flow prediction method based on decoupling fusion network
一种基于解耦融合网络的交通流量预测方法
2024-11-29
Patent
Electronic Resource
Chinese
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