We proposed a novel ramp metering algorithm embedding multi-agent deep reinforcement learning (DRL) techniques, based on the data of loop detectors. A multi-agent DRL framework is adopted to generate proper ramp metering scheme for each ramp meter in real time to improve the operation efficiency of urban freeway with less investment. A simulation platform is developed to simplify the implementation and training of the algorithm. A set of simulation experiments – encompassing both single and multi-ramp scenarios with various traffic demand profiles – are conducted. Comparing with the state-of-the-practice ramp metering methods, the simulation results demonstrate that the proposed DRL-based algorithm outperforms in a comprehensive evaluation index considering mainstream speed at bottleneck and queue size on ramp. The method presents robustness, scalability, and the capability of further improvement by online learning during implementation.


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    Title :

    Advanced Self-Improving Ramp Metering Algorithm based on Multi-Agent Deep Reinforcement Learning


    Contributors:
    Deng, Fuwen (author) / Jin, Jiandong (author) / Shen, Yu (author) / Du, Yuchuan (author)


    Publication date :

    2019-10-01


    Size :

    660517 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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