The invention discloses an urban road network traffic flow prediction method based on a K-shape clustering algorithm and a graph convolutional neural network. Original traffic flow data of a selected urban road network are obtained, and an urban road network traffic flow data set is obtained through data processing; a K-shape clustering algorithm is used to construct a sub-region division module of the urban road network traffic flow prediction method, urban sub-road network division is carried out on an urban road network, and prediction sub-regions with stronger traffic flow data correlation are obtained; selecting a graph convolutional neural network to construct a space module of the prediction method, and analyzing a traffic flow data space rule and a road network topological structure of each prediction subarea; a time module of the prediction method is constructed by using a long-short-term memory neural network, and a time rule of urban road network traffic flow data is mined; and introducing an output module of the full connection layer construction prediction method to obtain a pre-measured value of the urban road network traffic flow. Compared with an existing prediction method, the urban road network traffic flow prediction method can significantly improve the urban road network traffic flow prediction precision.

    一种基于K‑shape聚类算法和图卷积神经网络的城市路网交通流预测方法。获取选定城市路网的原始交通流数据,通过数据处理获得城市路网交通流数据集;使用K‑shape聚类算法构建城市路网交通流预测方法的子区划分模块,对城市路网进行城市子路网划分,获得交通流数据相关性更强的预测子区;选取图卷积神经网络搭建预测方法的空间模块,分析每个预测子区交通流数据空间规律和路网拓扑结构;利用长短期记忆神经网络构建预测方法的时间模块,挖掘城市路网交通流数据的时间规律;引入全连接层搭建预测方法的输出模块,获得城市路网交通流预的测值。相比现有预测方法,所提城市路网交通流预测方法能够显著地提升城市路网交通流预测精度。


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    Title :

    Urban road network traffic flow prediction method based on K-shape clustering algorithm and graph convolutional neural network


    Additional title:

    一种基于K-shape聚类算法和图卷积神经网络的城市路网交通流预测方法


    Contributors:
    ZHANG WENSONG (author) / JIANG ZHENRONG (author) / WANG YA (author) / YUAN YING (author) / LI JIANING (author) / DU GUOLIANG (author)

    Publication date :

    2024-12-03


    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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