This paper addresses mixed traffic flow involving autonomous vehicles and human-driven vehicles at signalized intersections. A deterministic discrete-time simulation model, the mixed traffic dynamics model, based on the assured clear distance ahead criterion (the ACDA-MTD model) is proposed. The ACDA-MTD model assumes each vehicle’s compliance with an operational strategy that is consistent with the “Assured Clear Distance Ahead” criterion, by which each vehicle adjusts speed and distance from the vehicle ahead to ensure safe traveling distance, regardless of the future behavior of the leading vehicle. Then we explore the impact of 23 different scenarios on intersection traffic capacity. The scenarios cover various proportions and orders that consist of autonomous and human-driven vehicles under different connected vehicle environments. Finally, the simulation analysis shows that the vehicle-to-vehicle (V2V) and the vehicle-to-infrastructure (V2I) technologies can improve the startup efficiency of autonomous vehicles at the intersection, and then enhance the capacity by 4.68%~28.63%.


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    Title :

    Autonomous and Connected Vehicles: The Capacity of Mixed Traffic Flow at Signalized Intersection with the ACDA-MTD Model


    Contributors:
    Li, Hanchu (author) / Zhang, Jian (author) / Zheng, Fangfang (author) / Li, Linchao (author) / Ran, Bin (author)

    Conference:

    18th COTA International Conference of Transportation Professionals ; 2018 ; Beijing, China


    Published in:

    CICTP 2018 ; 34-45


    Publication date :

    2018-07-02




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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