The collected crossroad data is clustered through an unsupervised machine learning algorithm to find the internal connections between different traffic intersections. This paper hopes that in the future traffic jams solving process, clustering algorithms can be used for reference to aggregate the similar traffic junction control experience into a same category, to guide relevant technical personnel to quickly find out the cause of the congestion problem in the urban traffic road network, thus formulating a reasonable and feasible traffic jam control plan based on the same category of traffic junction control experience.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Optimization and application of crossroad similarity matching based on the clustering algorithm


    Contributors:
    Wei, Lu (author) / Feng, Guanghui (author) / Jiahui, Chen (author)


    Publication date :

    2020-11-01


    Size :

    1885333 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    CROSSROAD ESTIMATION DEVICE

    AKAMINE YUSUKE / KONDO KATSUHIKO / MIYAKE YASUYUKI | European Patent Office | 2020

    Free access

    Crossroad transit system

    SONG CHANG HO | European Patent Office | 2021

    Free access

    CROSSROAD ESTIMATION DEVICE

    AKAMINE YUSUKE / KONDO KATSUHIKO / MIYAKE YASUYUKI | European Patent Office | 2020

    Free access

    Simulation of Crossroad Traffic

    Boris Tovornik / Drago Sever / Daniel Rogač | DOAJ | 2012

    Free access

    California at a crossroad

    Barrow, Keith | IuD Bahn | 2008