Autonomous vehicles (AVs) have the potential to minimize car crashes caused by human errors, reduce emissions to allow a sustainable environment, and provide smaller headways and reaction times to reduce congestion and facilitate mobility. Since AV adaptations are still in their early stages, the use of traffic modeling and simulation can justify the positive impacts of AVs on our transportation systems. This study aims to use the PTV VISSIM microscopic simulator to evaluate the impact of AVs on traffic mobility at an urban intersection in Stillwater, Oklahoma. To prepare for the VISSIM simulation, 15-min field traffic counts during the afternoon peak hour, signal timings, and the intersection geometry were collected at the intersection. Three AV behavior types (AV normal, AV cautious, and AV allknowing) were penetrated at different rates and compared with the conventional driving through several simulation scenarios. The simulation results demonstrate that AVs can significantly reduce the queue delay, stopped delay, travel time, and queue length, leading to an improvement in traffic flow and mobility.


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    Titel :

    Evaluating the Impact of Autonomous Vehicles on Traffic Flow at a Stillwater Intersection


    Beteiligte:
    Huang, Chenwei (Autor:in) / Li, Joshua Q. (Autor:in) / Liu, Wenyao (Autor:in) / Yang, Xue (Autor:in)

    Kongress:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Erscheinungsdatum :

    31.08.2022




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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