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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    Title :

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


    Contributors:


    Publication date :

    2014-10-01


    Size :

    1950332 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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