The safe and efficient merging of traffic flow at on-ramps is an important issue for ensuring reliable operation of the transportation system. This paper proposes a dual-layer Deep Reinforcement Learning (DRL) strategy for ramp traffic flow merging control. The upper level employs D3QN for decision timing determination, while the lower level uses DDPG to control the following speed of the vehicle platoon. Coupled training is conducted to enhance coordination. Combining with a distributed lane selection system, the convergence efficiency of high-density traffic flow at entrance ramps is improved, and the allocation of traffic flow between lanes is optimized, reducing the risk of accidents. Compared to single-strategy approaches such as D3QN, DDPG, and SAC, the dual-layer DRL strategy achieves a significant improvement in the average success rate of ramp merging tasks, with increases of 97.87%, 17.72%, and 29.17%, respectively. The study validates the effectiveness of the proposed strategy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dual-Layer Deep Reinforcement Learning for Lane Merging Control in On-Ramp Traffic


    Contributors:
    Zhou, Tong (author) / Huang, Yuzhao (author) / Tian, Yudan (author) / Huang, Hua (author) / Ou, Minghui (author)

    Conference:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Published in:

    CICTP 2024 ; 1697-1706


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Multi-Agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic

    Chen, Dong / Hajidavalloo, Mohammad R. / Li, Zhaojian et al. | IEEE | 2023


    ANTI-JERK ON-RAMP MERGING USING DEEP REINFORCEMENT LEARNING

    Lin, Yuan / McPhee, John / Azad, Nasser L. | British Library Conference Proceedings | 2020


    Deep reinforcement learning algorithm based ramp merging decision model

    Chen, Zeyu / Du, Yu / Jiang, Anni et al. | SAGE Publications | 2025


    Anti-Jerk On-Ramp Merging Using Deep Reinforcement Learning

    Lin, Yuan / McPhee, John / Azad, Nasser L. | IEEE | 2020


    LLM-Enhanced Reinforcement Learning for Traffic Control at On-Ramp Merging Areas

    Yin, Qihao / Xiong, Zhengang / Liu, Yanyue | Springer Verlag | 2025