As an important transportation channel and traffic hub, the highway is frequently congested due to the rapid growth of traffic demand. Therefore, this paper adopts the Mahalanobis distance, which is more sensitive to the change relationship and discreteness between different dimensional data, to replace the traditional Euclidean distance to improve the algorithm, and constructs a traffic congestion discrimination model based on the improved fuzzy C-means clustering algorithm. The model takes the measured data of the highway interchange section as the input, and discriminates the highway operation condition according to the clustering results. Through empirical verification, the discrimination effect of the model is significantly better than the traditional clustering algorithm and the current speed threshold discrimination method, and has good applicability and accuracy.


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    Titel :

    Highway interchange section state discrimination method based on improved fuzzy C-means clustering algorithm


    Beteiligte:
    Wu, Jinsong (Herausgeber:in) / Ma'aram, Azanizawati (Herausgeber:in) / Li, Wenyong (Autor:in) / Wang, Wenyu (Autor:in) / Li, Jiawei (Autor:in) / Liang, Yuyao (Autor:in) / Lian, Guan (Autor:in)

    Kongress:

    Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024) ; 2024 ; Guilin, China


    Erschienen in:

    Proc. SPIE ; 13251


    Erscheinungsdatum :

    28.08.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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