The invention discloses a smart city road traffic condition prediction method based on deep learning and time-space multi-source information fusion. The method comprises the following steps: coding nodes; a graph convolutional network is adopted; a time embedding module; performing dynamic space feature fusion; and performing prediction through a full-connection neural network. According to the method, the LSTM is combined into the network, so that past information about the flow state is reserved, and a multi-subgraph multi-region attention fusion result is processed.

    本发明公开了一种基于深度学习及时空多源信息融合的智慧城市道路交通状况预测方法,方法包括:对节点进行编码;采用图卷积网络;时间嵌入模块;动态空间特征融合;全连接神经网络进行预测。本发明提出了将LSTM组合到网络中,以保留过去关于流量状态的信息,并处理多子图多区域注意力融合结果。


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    Title :

    Smart city road traffic condition prediction method based on deep learning and time-space multi-source information fusion


    Additional title:

    基于深度学习及时空多源信息融合的智慧城市道路交通状况预测方法


    Contributors:
    ZHENG CHAO (author) / FU WENBO (author) / CAO QIUSHENG (author)

    Publication date :

    2025-01-03


    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 / 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



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