During road work, lane closures significantly reduce road capacity and negatively impact traffic safety in the upstream segments. This study introduces a merge control strategy for the work zone on freeway that aims to alleviate severe congestion and improve flow efficiency in environments where manual vehicles (MVs) and connected automated vehicles (CAVs) coexist. Using a short-term prediction model combined with a proportional-integral-derivative (PID) controller, this strategy dynamically adjusts merging behavior based on real-time traffic conditions. The PID controller calculates error values as the difference between current and target states, adjusting responses through proportional, integral, and derivative terms. The predictions of the traffic state based on the density of open lanes in each segment guide the controller’s decision to initiate a “Merge” or “No Merge” guidance. When merging is deemed necessary, the controller estimates the optimal number of vehicles to merge for each segment, using the severe congestion threshold as a reference point. This approach was tested using a microscopic simulation tool on a calibrated real-world network under mixed traffic conditions. The results indicate that the proposed strategy effectively disperses merging upstream, increases merging speeds, and maintains lane density below critical congestion levels, thus enhancing operation efficiency and safety in work zone areas.


    Access

    Download


    Export, share and cite



    Title :

    PID-Based Freeway Work Zone Merge Control with Traffic State Prediction under Mixed Traffic Flow of Connected Automated Vehicles and Manual Vehicles


    Contributors:
    Sunho Kim (author) / Yongju Kim (author) / Youngho Kim (author) / Chungwon Lee (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Driver Behavior at a Freeway Merge to Mixed Traffic of Conventional and Connected Autonomous Vehicles

    Chityala, Sneha / Sobanjo, John O. / Erman Ozguven, Eren et al. | Transportation Research Record | 2020