Non-green channel vehicles affect the normal operation and management of highways. This study investigates the different characteristics between green and non-green channel vehicles. The binary Logistic regression model was developed to screen suitable variables and identify the non-green channel vehicles rapidly and accurately. Data were collected from Liwen expressway in Jiangxi Province. The result indicated that the weight of vehicle and goods, the vehicles provinces, vehicle types, charge amount, and time information are contributing variables. And based on the model, 97.9% of the non-green channel vehicles were correctly classified, while 92.6% of the green channel vehicles were correctly classified. In addition, the non-green channel vehicles are more likely to pass through in the early morning and evening, while green channel vehicles have no obvious time preference. These findings from the study provide useful insights in detecting non-green channel vehicles rapidly.


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    Titel :

    Identification of Non-Green Channel Vehicles at Highway Toll Gate Based on Logistic Regression Model


    Beteiligte:
    Wang, Yuxuan (Autor:in) / Zhang, Jisheng (Autor:in) / Liu, Pan (Autor:in) / Xu, Chengcheng (Autor:in) / Zhang, Fan (Autor:in)

    Kongress:

    19th COTA International Conference of Transportation Professionals ; 2019 ; Nanjing, China


    Erschienen in:

    CICTP 2019 ; 2672-2682


    Erscheinungsdatum :

    02.07.2019




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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