The invention provides a traffic flow prediction method based on a graph neural network, and aims to solve the problems of space-time dependence capture and dynamic relevance modeling of traffic data. According to the method, a dynamic graph convolutional network is constructed, time and space features are synchronously captured by using an interactive learning strategy, and the model calculation efficiency is improved by fusing a probability sparse self-attention mechanism. The method can effectively predict the future traffic flow, has high prediction precision and calculation efficiency, and is widely applied to the fields of intelligent traffic systems, urban traffic management and the like.

    本发明提供了一种基于图神经网络的交通流量预测方法,旨在解决交通数据的时空依赖捕捉和动态关联性建模问题。所述方法通过构建动态图卷积网络,利用交互式学习策略同步捕捉时间和空间特征,并通过融合概率稀疏自注意机制提高模型计算效率。该方法能够有效预测未来的交通流量,具有较高的预测精度和计算效率,广泛适用于智能交通系统、城市交通管理等领域。


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    Titel :

    Traffic flow prediction method based on graph neural network


    Weitere Titelangaben:

    一种基于图神经网络的交通流量预测方法


    Beteiligte:
    PENG LAIHU (Autor:in) / WU BAOWEN (Autor:in) / QI YUBAO (Autor:in) / HOU LIANGMEI (Autor:in) / YAN HUAJIAN (Autor:in)

    Erscheinungsdatum :

    26.11.2024


    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 / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung



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