The invention discloses a traffic flow prediction model training method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical traffic flow data, historical traffic density data and historical traffic network topology structure data; performing deep learning prediction based on the historical traffic flow data, the historical traffic density data and the historical traffic network topology structure data to obtain a first flow prediction result; performing cross attention mapping on the historical traffic flow data and the historical traffic density data to obtain a second flow prediction result; and determining model loss based on the first flow prediction result and the second flow prediction result, updating initial neural network parameters based on the model loss until a model training stop condition is satisfied, and obtaining a traffic flow prediction model. According to the technical scheme, the generalization of the prediction precision of the traffic flow prediction model is improved.

    本发明公开了一种交通流量预测模型的训练方法、装置、电子设备及存储介质。该方法包括:获取历史交通流量数据、历史交通密度数据和历史交通网络拓扑结构数据;基于历史交通流量数据、历史交通密度数据和历史交通网络拓扑结构数据进行深度学习预测,得到第一流量预测结果;对历史交通流量数据和历史交通密度数据进行交叉注意力映射,得到第二流量预测结果;基于所述第一流量预测结果和所述第二流量预测结果确定模型损失,基于所述模型损失更新初始神经网络参数,直至满足模型训练停止条件,得到交通流量预测模型。上述技术方案,提升了交通流量预测模型的预测精度的泛化性。


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    Title :

    Training method and device of traffic flow prediction model, electronic equipment and storage medium


    Additional title:

    交通流量预测模型的训练方法、装置、电子设备及存储介质


    Contributors:
    JIANG RUIYUAN (author) / JIA DONGYAO (author) / DONG DAPENG (author) / LIN YONGYI (author)

    Publication date :

    2025-03-25


    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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