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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Deng, Fuwen (Autor:in) / Jin, Jiandong (Autor:in) / Shen, Yu (Autor:in) / Du, Yuchuan (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    660517 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

    Belletti, Francois / Haziza, Daniel / Gomes, Gabriel et al. | IEEE | 2018


    Self-Learning Adaptive Ramp Metering

    Rezaee, Kasra / Abdulhai, Baher / Abdelgawad, Hossam | Transportation Research Record | 2013




    Coordinated Ramp Metering with Equity Consideration Using Reinforcement Learning

    Lu, Chao / Huang, Jie / Deng, Lianbo et al. | ASCE | 2017