Traffic congestion is a growing problem worldwide causing time/fuel waste, pollution, and even stress. Various approaches have been proposed to reduce traffic jams. Recently, researchers have started to employ connected vehicle (CV) technology. Most solutions, however, rely on a binary approach to determine a traffic jam, i.e., whether it exists or not. Accordingly, output given to a driver in the form of driving advisory also tends to be binary and static. However, a traffic jam is a dynamic phenomenon, the intensity of which changes over time depending on various factors including randomness of driving behavior and road conditions. In this paper, we propose to integrate a fuzzy inference system into a traffic-jam-control algorithm such that the dynamics of a traffic jam is effectively represented, thereby providing diversified driving advisory depending upon the intensity of a traffic jam. Through simulations, it is shown that the integrated approach reduces traffic delay by up to 6.5% compared with the state-of-the-art solution.


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    Titel :

    FuzzyJam: Reducing traffic jams using a fusion of fuzzy logic and vehicular networks


    Beteiligte:
    Won, Myounggyu (Autor:in) / Park, Taejoon (Autor:in) / Son, Sang H. (Autor:in)


    Erscheinungsdatum :

    01.10.2014


    Format / Umfang :

    1950332 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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