The invention provides an intelligent flow prediction method for an expressway, and the method comprises the steps: collecting the traffic information data, meteorological data and expressway repair congestion information of an expressway portal in unit time, and constructing an expressway traffic flow time sequence information data set and a multi-parameter time sequence data set after data preprocessing; establishing a portal frame network structure topological graph according to the relative geographic position information of the expressway portal frame; constructing an Informer neural network model, encoding a highway road network topological structure and traffic flow time sequence information, and learning time and space dependence feature information of data; an XGBoost regression model is constructed, features such as weather and congestion are learned, and weighting is carried out on the features and an Informer model result. According to the method, the expressway traffic flow prediction capability of the model is improved by combining deep learning and machine learning.

    本发明提出了一种用于高速公路的智能流量预测方法,包括:采集单位时间内高速公路门架的通行信息数据、气象数据和高速公路修路拥堵信息,并在数据预处理后构建高速公路交通流量时序信息数据集和多参数时序数据集;根据高速公路门架的相对地理位置信息建立门架网络结构拓扑图;构建Informer神经网络模型,对高速公路路网拓扑结构和交通流量时序信息进行编码并学习数据的时间空间依赖特征信息;构建XGBoost回归模型,对气象、拥堵等特征进行学习,并与Informer模型结果做加权。本发明通过结合深度学习和机器学习来提高模型的高速公路交通流量预测能力。


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    Titel :

    Intelligent traffic prediction method for expressway


    Weitere Titelangaben:

    一种用于高速公路的智能流量预测方法


    Beteiligte:
    ZHAN ENNING (Autor:in) / ZHU RUIXIN (Autor:in) / LI CHAOFAN (Autor:in) / HOU XIAOXU (Autor:in) / ZHANG HANG (Autor:in) / MA TIANMING (Autor:in) / CHEN LING (Autor:in)

    Erscheinungsdatum :

    20.10.2023


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    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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