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.
Advanced Self-Improving Ramp Metering Algorithm based on Multi-Agent Deep Reinforcement Learning
2019-10-01
660517 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Self-Learning Adaptive Ramp Metering
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