本发明提供一种基于时间注意力循环图卷积神经网络的交通流量预测方法,涉及数据处理技术领域。该方法包括:获取待预测的交通流量数据。对待预测的交通流量数据进行预处理,并获取交通流量信号矩阵。对交通流量信号矩阵进行空间特征提取,获取待预测的交通流量数据的第一特征数据。对第一空间特征数据进行时间特征提取,获取待预测的交通流量数据的第二特征数据。对第二特征数据进行时间特征提取,获取待预测的交通流量数据的第三特征数据。以及,将待预测的交通流量数据的第三特征数据作为输入,获取交通流量预测模型的输出结果,根据交通流量预测模型的输出结果获取交通流量预测结果。该方法能够提高交通流量预测精度。

    The invention provides a traffic flow prediction method based on a time attention cycle graph convolutional neural network, and relates to the technical field of data processing. The method comprises the following steps: acquiring traffic flow data to be predicted; traffic flow data to be predicted are preprocessed, and a traffic flow signal matrix is obtained. And performing spatial feature extraction on the traffic flow signal matrix to obtain first feature data of the traffic flow data to be predicted. And performing time feature extraction on the first spatial feature data to obtain second feature data of the to-be-predicted traffic flow data. And performing time feature extraction on the second feature data to obtain third feature data of the to-be-predicted traffic flow data. And taking the third feature data of the traffic flow data to be predicted as input, obtaining an output result of the traffic flow prediction model, and obtaining a traffic flow prediction result according to the output result of the traffic flow prediction model. The method can improve the traffic flow prediction precision.


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    Titel :

    基于时间注意力循环图卷积神经网络的交通流量预测方法


    Erscheinungsdatum :

    11.07.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS