The invention discloses a road network traffic flow prediction method based on a gated loop graph convolution attention network, and the method comprises the following steps: 1, obtaining traffic flow data collected by a plurality of sensor nodes, and obtaining the traffic flow data of a target region and an adjacent matrix of the sensor nodes; 2, preprocessing the traffic flow data of the target area, dividing the traffic flow data into a training set, a verification set and a test set, and generating a traffic flow data sequence corresponding to each set; step 3, constructing a gated loop graph convolution attention network, training by adopting the training set, and checking the convergence condition by adopting the verification set, thereby completing the training of the gated loop graph convolution attention network; and step 4, inputting the test set into the trained gated loop graph convolution attention network to obtain a traffic flow prediction result. The method can effectively extract the time characteristics of the traffic flow, reduces the operand, and improves the model prediction precision.

    本发明公开了基于门控循环图卷积注意力网络的路网交通流量预测方法,包括以下步骤:步骤1、获取多传感器节点采集的交通流量数据,得到目标区域交通流量数据以及传感器节点的邻接矩阵;步骤2、对目标区域交通流量数据进行预处理后划分为训练集、验证集、测试集,生成各集对应的交通流量数据序列;步骤3、构建门控循环图卷积注意力网络,采用训练集进行训练,并采用验证集检验收敛情况,由此完成对门控循环图卷积注意力网络的训练;步骤4、将测试集输入至训练好的门控循环图卷积注意力网络,得到交通流量预测结果。本发明能够有效提取交通流量的时间特征,减少了运算量,提高了模型预测精度。


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    Title :

    Road network traffic flow prediction method based on gated loop graph convolution attention network


    Additional title:

    基于门控循环图卷积注意力网络的路网交通流量预测方法


    Contributors:
    REN YILONG (author) / CHEN YUE (author) / MA TIAN (author) / YU HAIYANG (author) / CUI ZHIYONG (author)

    Publication date :

    2023-12-08


    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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