Prior work has shown that while vehicles equipped with Adaptive Cruise Control (ACC) algorithms have the ability to improve traffic flows by increasing the critical density at which traffic jams begin to occur, they remain highly susceptible to the presence of even a few human-driven vehicles. This necessitates a trade-off assessment between improving traffic flow and reducing its susceptibility to presence of human-driven vehicles. In this paper, we address this issue via Cooperative Adaptive Cruise Control (CACC) algorithms that use information communicated via other vehicles or infrastructure. Specifically, the CACC algorithms use a modified form of the General Motors' car-following model, where the driver sensitivity is a function of the size of the existing traffic jam. Analysis using the master equation approach shows that, by altering its response based on the existing traffic state, the following connected vehicle is able to mitigate jam formation for a wide range of vehicular densities. Moreover, since the following connected vehicle can leverage information across larger distances, the ‘effective’ critical density is higher than previous results developed with ACC-enabled vehicles only. The included results indicate that traffic state-dependent CACC algorithms can improve traffic flow, and also hold significant potential to reduce susceptibility to the presence of human-driven vehicles.


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    Title :

    Mitigation of self-organized traffic jams using cooperative adaptive cruise control


    Contributors:


    Publication date :

    2016-09-01


    Size :

    251373 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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