Taiwan freeway electronic toll collection system (ETC) is not only replacing manual tolling to high accuracy distance-based toll collection ways, but is collecting specific and timely traffic data. In this study, we utilized the open data from ETC, such as traffic flow information, travel time information, vehicle types, etc., in every 5 minutes for multiple years and used the support vector machine and support vector regression method to get the kernel function to predict the future travel time and future traffic flow. Furthermore, we applied these prediction values as important references to support the strategies decision in freeway road maintenance management system and perform in the freeway management system. Thus, with the help of the big data collection and utilization we could efficiently improve the freeway service performance and develop the freeway system into intelligent transportation system (ITS) in the near future.


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    Titel :

    Analysis of Priority Maintain Segmentation in Pavement Maintenance Management for National Freeways


    Beteiligte:
    Lin, Jyh-Dong (Autor:in) / Liu, Pin-Liang (Autor:in) / Hsu, Shih-Ming (Autor:in) / Ho, Min-Che (Autor:in)

    Kongress:

    17th COTA International Conference of Transportation Professionals ; 2017 ; Shanghai, China


    Erschienen in:

    CICTP 2017 ; 178-183


    Erscheinungsdatum :

    18.01.2018




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch