Estimating highway traffic density in real-time is an important goal of Intelligent Transportation Systems. The main contribution of this work is to propose a simple and entirely V2V-based methodology to estimate, in real-time, traffic density based on data collected by moving observers in co-directional and on- coming traffic. To estimate traffic density in a privacy-preserving manner, our methodology uses tallies consisting of the number of times a vehicle is passed by other vehicles, minus the number of times it passes other vehicles. As it turns out, keeping tallies is a non-trivial task since vehicles are allowed to vary their speed in arbitrary ways and, as a result, the same two vehicles may pass each other any number of times. We provide a detailed proof of correctness of our methodology; to assess its accuracy, we have performed extensive simulations and sensitivity analyses using SUMO-generated synthetic traffic traces over a wide range of penetration rates and traffic flows.


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    Titel :

    Real-Time Traffic Density Estimation: Putting on-Coming Traffic to Work


    Beteiligte:
    Florin, Ryan (Autor:in) / Olariu, Stephan (Autor:in)


    Erscheinungsdatum :

    01.01.2023


    Format / Umfang :

    1163936 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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