The invention relates to a traffic flow prediction method based on a space-time diagram attention neural network, comprising the following steps: acquiring traffic flow data, inputting the traffic flow data into a trained traffic flow prediction model to obtain a prediction result, the traffic flow prediction model comprising a plurality of encoders and a decoder, traffic flow data enters the first encoder after passing through the embedded unit, the input of each encoder is the output and the initial graph of the previous encoder, the input of the first encoder is the traffic flow data and the initial graph after passing through the embedded unit, and each encoder comprises a space attention module and a time attention module. Compared with the prior art, the method has the advantages of improving the prediction precision and the like.

    本发明涉及一种基于时空图注意力神经网络的交通流量预测方法,包括以下步骤:获取交通流量数据,将交通流量数据输入训练好的交通流量预测模型中,得到预测结果,其中,交通流量预测模型包括多个编码器和一个解码器,交通流量数据经过嵌入单元后进入第一个编码器,每个编码器的输入为上一个编码器的输出和初始图,第一个编码器的输入为经过嵌入单元后的交通流量数据和初始图,编码器包括空间注意模块和时间注意模块。与现有技术相比,本发明具有提高预测精度等优点。


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    Title :

    Traffic flow prediction method based on time-space diagram attention neural network


    Additional title:

    基于时空图注意力神经网络的交通流量预测方法


    Contributors:
    LI BAILIN (author) / WEN MI (author)

    Publication date :

    2025-03-11


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

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



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