The invention discloses a traffic flow prediction method and system under data missing based on tensor decomposition, and belongs to the technical field of intelligent traffic, and the method comprises the steps: obtaining a spatial adjacency matrix of a traffic road network and traffic flow containing missing values, and inputting the spatial adjacency matrix and the traffic flow into a pre-trained tensor graph convolutional network model for prediction; comprising the following steps: constructing a time adjacency matrix according to the space adjacency matrix and traffic flow containing missing values; the traffic flow containing the missing value is converted into a tensor, tensor decomposition is carried out, and a feature tensor is obtained through decomposition; inputting the spatial adjacency matrix, the temporal adjacency matrix and the feature tensor into an image convolution layer, and outputting a prediction feature; and performing linear conversion on the prediction features, and outputting a model prediction result to obtain predicted traffic flow. The Tucker decomposition and the graph convolutional neural network are combined to construct the traffic flow prediction model, so that the model can predict the traffic flow more accurately under the condition of data missing.
本发明公开了一种基于张量分解的数据缺失下交通流量预测方法及系统,属于智能交通技术领域,获取交通路网的空间邻接矩阵和包含缺失值的交通流量输入至预先训练好的张量图卷积网络模型进行预测,包括:根据所述空间邻接矩阵和包含缺失值的交通流量构建时间邻接矩阵;将包含缺失值的交通流量转化为张量并进行张量分解,分解得到特征张量;将所述空间邻接矩阵、时间邻接矩阵和特征张量输入至图卷积层中,输出预测特征;将所述预测特征进行线性转换,输出模型预测结果,得到预测的交通流量。将Tucker分解与图卷积神经网络结合构建交通流量预测模型,在数据缺失情况下,使得模型可以更准确地预测交通流量。
Traffic flow prediction method and system under data loss based on tensor decomposition
一种基于张量分解的数据缺失下交通流量预测方法及系统
2024-08-30
Patent
Electronic Resource
Chinese
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