The invention discloses a traffic road condition prediction method, a classification model training method and a computer readable storage medium, and the method comprises the steps: obtaining the cell load data and user terminal switching data of a target cell in a target time period, and enabling the target cell to be a cell with a signal covering a target road section; constructing a target feature vector according to the cell load data and the user terminal switching data; and inputting the target feature vector into a preset classification model for identification, and obtaining the congestion level of the target road section in the target time period, the preset classification model is obtained by training by taking a feature vector constructed by load data of a cell and user terminal switching data as model input data and taking label data constructed by an actual congestion level of a road section covered by the cell as a model training label.

    本申请公开了一种交通路况预测方法、分类模型的训练方法及计算机可读存储介质,其中,方法包括:获取目标小区在目标时段的小区负荷数据和用户终端切换数据,其中,目标小区为信号覆盖目标道路路段的小区;根据小区负荷数据和用户终端切换数据构建目标特征向量;将目标特征向量输入预设分类模型进行识别,得到目标道路路段在目标时段内的拥堵等级,其中,预设分类模型是以小区的负荷数据和用户终端切换数据构建的特征向量为模型输入数据、以小区所覆盖的道路路段的实际拥堵等级构建的标签数据为模型训练标签进行训练得到的。


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    Titel :

    Traffic road condition prediction method, classification model training method and medium


    Weitere Titelangaben:

    交通路况预测方法、分类模型的训练方法及介质


    Beteiligte:
    ZHOU XIANWEN (Autor:in) / LIU QIAOYAN (Autor:in) / WANG KAI (Autor:in) / LI JIANGUO (Autor:in)

    Erscheinungsdatum :

    27.12.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / H04W WIRELESS COMMUNICATION NETWORKS , Drahtlose Kommunikationsnetze / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung



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