The invention discloses a time-space synchronization graph convolutional neural network and traffic prediction method for smart traffic. The method comprises the following steps: acquiring historical traffic data for preprocessing to obtain a traffic data sample and an adjacent matrix; inputting the sample input and the adjacent matrix into a preset trained target network model, and generating a local space-time diagram and an adjacent matrix thereof by a local space-time construction module; the local space-time diagram and the adjacent matrix thereof are input into a lower-layer space-time synchronous extraction module for feature extraction, and a local space-time feature matrix is generated; and inputting into a lower-layer time sequence analysis module, carrying out global spatial-temporal characteristic analysis, and obtaining a final prediction value. The time-space synchronization graph convolutional neural network of the intelligent traffic system and the traffic prediction method provided by the invention have better effects in resisting interference of random events, realizing global analysis of features, and extracting feature extraction capability of association between time features and spatial features in traffic data.

    本发明公开了一种智慧交通的时空同步图卷积神经网络及交通预测方法包括:获取历史交通数据进行预处理,得到交通数据样本及邻接矩阵;将所述样本输入及邻接矩阵输入至预设训练的目标网络模型中,由局部时空构建模块生成局部时空图及其邻接矩阵;局部时空图及其邻接矩阵输入至下层时空同步提取模块中,进行特征提取,生成局部时空特征矩阵;输入至下层时序分析模块中,进行全局时空特征分析,并获得最终预测值。本发明提供的智慧交通系统的时空同步图卷积神经网络及交通预测方法在对抗随机事件的干扰,实现特征的全局分析,提取交通数据中时间特征与空间特征之间的关联的特征提取能力都取得更加良好的效果。


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    Title :

    Intelligent traffic space-time synchronization graph convolutional neural network and traffic prediction method


    Additional title:

    一种智慧交通的时空同步图卷积神经网络及交通预测方法


    Contributors:
    ZHAO JINBO (author) / XU XIAOLONG (author)

    Publication date :

    2023-03-28


    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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