The invention provides a key road section recognition method based on two-stage feature learning, and the method comprises the steps: obtaining the static and dynamic embedding of a road section through a natural language processing technology based on mobile phone positioning data and road network data, and carrying out the recognition of a key road section through a two-stage feature learning method based on attention pooling and differential clustering. The road sections have obvious influence on traffic flow of a whole urban road network or a local area under a normal road condition; and obtaining a key road section identification result. Therefore, the road sections which have obvious influence on the traffic flow of the whole urban road network or the local area under the normal road condition can be identified. The method not only comprises road sections with relatively large influence in the global situation, but also covers relatively key road sections which have relatively small traffic flow but play an important connection role in a local area.

    本发明提供一种基于两阶段特征学习的关键路段识别方法,基于手机定位数据和路网数据,利用自然语言处理技术获取路段静态和动态嵌入,并利用基于注意力池化和可微分聚类的两阶段特征学习方法,以识别出在正常路况下对整个城市路网或局部区域交通流动产生显著影响的路段;从而获取关键路段识别结果。以识别出在正常路况下对整个城市路网或局部区域交通流动产生显著影响的路段。这不仅包括在全局内影响力较大的路段,还涵盖那些虽交通流量较小,但在局部区域中起重要连接作用的相对关键路段。


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    Title :

    Key road section identification method based on two-stage feature learning


    Additional title:

    基于两阶段特征学习的关键路段识别方法


    Contributors:
    WU SHENG (author) / WU WEIYI (author)

    Publication date :

    2024-12-31


    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 / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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