The invention discloses a subway short-time passenger flow volume prediction method based on a space-time diagram convolutional network (GCN, Graph Convolutional Network). The method comprises the following steps: acquiring subway historical data; the method comprises the following steps: learning subway historical data by using a gating circulation unit to obtain a time dependency relationship of a subway network, and obtaining a hidden state Ht implying historical passenger flow volume change characteristics; and obtaining a dynamic spatial dependency relationship of the subway network by using the graph convolutional neural network so as to predict the passenger flow volume at the future moment. When space-time prediction is carried out on the urban subway passenger flow volume, in addition to the time dependency relationship of the subway network, the dynamic change characteristics of the space dependency relationship are also considered, and the passenger flow volume of all subway stations in the urban subway network at the (t+1) moment can be obtained by utilizing first-order approximate Cheb graph convolution.

    本发明公开一种基于时空图卷积网络(GCN,Graph Convolutional Network)的地铁短时客流量预测方法。所述的方法包括下述步骤:采集地铁历史数据;利用门控循环单元学习地铁历史数据以获取地铁网络的时间依赖性关系,得到一个隐含历史客流量变化特征的隐藏状态Ht;利用图卷积神经网络获取地铁网络的动态空间依赖性关系,以对未来时刻客流量进行预测。本发明对城市地铁客流量进行时空预测时,除了考虑地铁网络的时间依赖性关系外,还考虑到了空间依赖性关系的动态变化特征,利用一阶近似Cheb图卷积可得t+1时刻城市地铁网络中所有地铁站点的客流量


    Access

    Download


    Export, share and cite



    Title :

    Subway short-time passenger flow prediction method based on space-time diagram convolutional network


    Additional title:

    一种基于时空图卷积网络的地铁短时客流量预测方法


    Contributors:
    ZHONG SHAOBO (author) / ZHU WEI (author) / XU MIN (author) / RUI JING (author) / LIU SHUANGQING (author) / YIN MENGMENG (author)

    Publication date :

    2023-01-17


    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 / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / 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



    Traffic flow prediction method based on space-time diagram convolutional network

    JIANG CONG / SONG YUN / DENG ZELIN et al. | European Patent Office | 2023

    Free access

    Traffic flow prediction method based on space-time diagram convolutional network

    JIANG YUNLIANG / XIA RENHUAN / ZHANG XIONGTAO et al. | European Patent Office | 2022

    Free access

    Hybrid models of subway passenger flow prediction based on convolutional neural network

    Yuan‐wen Lai / Yang Wang / Xin‐ying Xu et al. | DOAJ | 2023

    Free access

    Hybrid models of subway passenger flow prediction based on convolutional neural network

    Lai, Yuan‐wen / Wang, Yang / Xu, Xin‐ying et al. | Wiley | 2023

    Free access

    Traffic flow prediction method of space-time diagram convolutional network

    TENG FEI / WANG ZIDAN / QIAO LU et al. | European Patent Office | 2024

    Free access