In order to identify the traffic state of urban expressway more accurately, a traffic state recognition model based on K-means clustering algorithm and AdaBoost integrated with multiple decision stump classifiers (AdaBoost-DS) is proposed. Taking the traffic flow, velocity and occupancy as the basic parameters, combined with the existing research results, the expressway traffic state is divided into four categories, and the K-means clustering algorithm is used to classify the traffic state; then, the classified traffic flow data are used to train the AdaBoost-DS model. The example verification and comparative analysis of the measured data of urban expressway show that the method in this paper is effective and feasible, and its recognition accuracy is 93.2%, which is 7.6% higher than that of BPNN.


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    Titel :

    Traffic state recognition of urban expressway based on K-means clustering and AdaBoost-DS


    Beteiligte:
    Gao, Mengyuan (Autor:in) / Chen, Xiufeng (Autor:in) / Chen, Ziyu (Autor:in) / Wang, Ruicong (Autor:in)

    Kongress:

    Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022) ; 2022 ; Guangzhou,China


    Erschienen in:

    Proc. SPIE ; 12591


    Erscheinungsdatum :

    16.02.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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