Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls.


    Access

    Download


    Export, share and cite



    Title :

    A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data


    Contributors:
    Bing Liu (author) / Yu Tang (author) / Yuxiong Ji (author) / Yu Shen (author) / Yuchuan Du (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Reinforcement Learning Control for On-Ramp Metering Based on Traffic Simulation

    Wang, X. / Liu, B. / Niu, X. et al. | British Library Conference Proceedings | 2009


    Reinforcement Learning Control for On-Ramp Metering Based on Traffic Simulation

    Wang, Xingju / Liu, Bohang / Niu, Xueqin et al. | ASCE | 2009


    Reinforcement Learning Ramp Metering Based on Traffic Simulation Model with Desired Speed

    Wang, Xingju / Bao, Jingang / Wang, Mingsheng et al. | ASCE | 2009


    Reinforcement Learning Ramp Metering Based on Traffic Simulation Model with Desired Speed

    Wang, X. / Bao, J. / Wang, M. et al. | British Library Conference Proceedings | 2009