The invention discloses a traffic flow prediction method based on transformer and multi-graph fusion convolution, and the method comprises the steps: inputting traffic flow data into a transformer module in a preset multi-graph convolution network model for processing, and obtaining long-term time correlation feature data; processing the traffic speed data based on a multi-graph fusion module in a preset multi-graph convolutional network model to obtain static and dynamic spatial correlation feature data in the road network; performing feature aggregation processing on the long-term time correlation feature data and the static and dynamic space correlation feature data according to a space-time diagram convolution module in a preset multi-diagram convolution network model to obtain space-time feature data; and analyzing and processing the spatial-temporal characteristic data through an output layer in a preset multi-graph convolutional network model to obtain traffic flow data in the next hour. The problem that dynamic spatial correlation in a road network cannot be captured in the prior art can be solved, and prediction errors are reduced.

    本申请公开了一种基于transformer和多图融合卷积的交通流量预测方法,包括:将交通流量数据输入预设多图卷积网络模型中的transformer模块进行处理,得到长期时间相关性特征数据;基于预设多图卷积网络模型中的多图融合模块对交通速度数据进行处理,得到道路网络中的静态与动态空间相关性特征数据;依据预设多图卷积网络模型中的时空图卷积模块对长期时间相关性特征数据和静态与动态空间相关性特征数据进行特征聚合处理,得到时空特征数据;通过预设多图卷积网络模型中的输出层对时空特征数据进行分析处理,得到未来一小时内的交通流量数据。本申请能解决现有技术无法捕获道路网络中的动态空间相关性的问题,减小预测误差。


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    Titel :

    Transform and multi-graph fusion convolution-based traffic flow prediction method


    Weitere Titelangaben:

    一种基于transformer和多图融合卷积的交通流量预测方法


    Beteiligte:
    LIU YIJUN (Autor:in) / HUANG HAO (Autor:in) / YE WUJIAN (Autor:in) / TAN DONGJUN (Autor:in)

    Erscheinungsdatum :

    19.07.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    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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