The invention discloses a traffic flow prediction method based on a graph neural network and distribution representation matching, and the method specifically comprises the following steps: collecting the traffic flow information of a region, and making a training set, a verification set and a test set; according to distribution distance maximization, historical traffic flow data of the training set is divided into different sub-domains, so that distribution among the sub-domains is diversified; constructing a graph structure adjacency matrix fusing different sub-domain information; constructing network extraction features; and calculating distribution matching loss for the hidden features, and learning distribution generality of different sub-domains. And training the model, optimizing parameters, and storing an optimal model on the verification set. And predicting test set data by using the trained model, and evaluating accuracy and performance. According to the method, the association relationship among the nodes in the non-stationary traffic data can be fully mined, and the prediction accuracy is improved.

    本发明公开了一种基于图神经网络和分布表征匹配的交通流量预测方法,具体步骤如下:收集地区的交通流量信息,制作训练集、验证集、测试集;根据分布距离最大化,对训练集的历史交通流量数据划分出不同的子域,使各个子域之间的分布多样化;构造出一个融合不同子域信息的图结构邻接矩阵;构建网络提取特征;对隐藏特征计算分布匹配损失,学习不同子域的分布共性。训练模型,优化参数,并在验证集上保存最优模型。用训练好的模型预测测试集数据,评估准确性和性能。本发明能够充分地挖掘非平稳交通数据中各节点之间的关联关系,提高预测准确性。


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    Title :

    Traffic flow prediction method based on graph neural network and distribution representation matching


    Additional title:

    基于图神经网络和分布表征匹配的交通流量预测方法


    Contributors:
    XIE ZONGXIA (author) / WANG JIAYI (author)

    Publication date :

    2024-07-26


    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 / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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