The invention aims to solve the problems of time-space dependence, space sparsity and the like in urban road traffic flow prediction modeling. An end-to-end deep learning model STNN (Spatial TemporaryNeural Neural Neural Networks) is constructed to carry out modeling on a road traffic flow mode; traffic flow distribution of all road sections in the whole city range at the future moment can be predicted with high precision according to historical flow data. The model can effectively extract the space-time mode in the road traffic flow, can effectively solve the problem of space sparsity of theroad traffic flow, and provides an effective solution for predicting the urban global road traffic flow.

    本发明针对城市道路交通流量预测建模中时空依赖性和空间稀疏性等问题,构建了一个端到端的深度学习模型STNN(Spatial Temporal Neural Networks)来对道路交通流模式进行建模,能够根据历史流量数据,以较高的精度预测未来时刻整个城市范围之内的所有道路路段的交通流分布。该模型能够有效提取道路交通流中的时空模式,并且能够有效解决道路交通流的空间稀疏性问题,为城市全域级的道路交通流的预测提供了一种行之有效的解决方法。


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    Titel :

    Urban road traffic flow prediction method and device based on space-time deep learning model


    Weitere Titelangaben:

    一种基于时空深度学习模型的城市道路交通流预测方法及装置


    Beteiligte:
    JIA TAO (Autor:in) / YAN PENGGAO (Autor:in)

    Erscheinungsdatum :

    2020-04-14


    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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