The invention discloses a traffic flow prediction method based on a global diffusion convolution residual network, and belongs to the technical field of intelligent traffic systems. The method comprises the following steps of: 1, establishing a traffic prediction model based on a global diffusion convolution residual network; 2, learning dynamic correlation and local and global spatial correlation; 3, capturing time correlation and global space-time correlation; and 4, fusing branch results and outputting a final result. According to the traffic flow prediction method, a global diffusion convolution residual network is provided, the model is composed of a plurality of periodic branches with the same structure, and the global attention diffusion convolution network and the global residual network of each branch are used for obtaining the spatial-temporal correlation of each period. Particularly, the global attention diffusion convolution network uses a PPMI matrix based on an attentionmechanism to capture dynamic space-time correlation, and the global residual network uses gating convolution and a global residual unit to capture time correlation and global space-time correlation atthe same time, so that the precision and efficiency of traffic prediction are improved.

    一种基于全局扩散卷积残差网络的交通流预测方法,属于智能交通系统技术领域。包括如下步骤:步骤1,建立基于全局扩散卷积残差网络的交通预测模型;步骤2,学习动态相关性、局部和全局空间相关性;步骤3,捕获时间相关性和全局时空相关性;步骤4,融合分支结果及输出;本交通流预测方法中,提出全局扩散卷积残差网络,该模型由多个具有相同结构的周期性分支组成,每个分支的全局注意力扩散卷积网络和全局残差网络来获取每个周期的时空相关性。特别地,全局注意力扩散卷积网络应用基于注意力机制的PPMI矩阵捕获动态的时空相关性,全局残差网络应用门控卷积和全局残差单元同时捕获时间相关性和全局的时空相关性,以此提高交通预测的精度和效率。


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    Title :

    Traffic flow prediction method based on global diffusion convolution residual network


    Additional title:

    一种基于全局扩散卷积残差网络的交通流预测方法


    Contributors:
    ZHENG KAI (author) / YE GUANYU (author) / LI YUANMING (author) / SUN FUZHEN (author) / LIU CONG (author)

    Publication date :

    2020-12-11


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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