The invention discloses a traffic flow prediction method based on a U-shaped multi-scale space-time diagram convolutional network, the U-shaped multi-scale space-time diagram convolutional network comprises a space-time encoder and a space-time decoder, and the method comprises the following steps: firstly, obtaining historical traffic flow data of each node in a traffic network in a preset time period; the method comprises the following steps: constructing original feature data including a channel dimension, a node dimension and a time dimension, then inputting the original feature data into a space-time encoder, extracting space-time features, then inputting the space-time features into a space-time decoder, performing jump connection on each decoding layer of the space-time decoder and a corresponding encoding layer in the space-time encoder, and finally obtaining a prediction result. According to the method, the space-time dependency relationship on different scales can be comprehensively captured, and better prediction performance can be obtained in a plurality of prediction time point step lengths.

    本发明公开了一种基于U形多尺度时空图卷积网络的交通流量预测方法,所述U形多尺度时空图卷积网络包括时空编码器和时空解码器,首先按获取交通网络中各个节点在预设时间段内的历史交通流量数据,构建包括通道维度、节点维度和时间维度的原始特征数据,然后将原始特征数据输入到时空编码器,提取时空特征,然后输入到时空解码器,时空解码器每一个解码层与时空编码器中对应的编码层跳跃连接,最后得到预测结果。本发明能够全面捕获不同尺度上的时空依赖关系,可以在多个预测时间点步长中获得较好的预测性能。


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    Title :

    Traffic flow prediction method based on U-shaped multi-scale space-time diagram convolutional network


    Additional title:

    基于U形多尺度时空图卷积网络的交通流量预测方法


    Contributors:
    ZHANG SHUAI (author) / YU WANGZHI (author) / SONG XIAOBO (author) / YAO JIAWEI (author) / ZHANG WENYU (author)

    Publication date :

    2023-09-01


    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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