A dynamic self-improving ramp metering algorithm based on multi-agent deep reinforcement learning
We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in r...
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| Vydáno v: | Transportation letters Ročník ahead-of-print; číslo ahead-of-print; s. 1 - 9 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Taylor & Francis
08.08.2024
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| ISSN: | 1942-7867, 1942-7875 |
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| Abstract | We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation. |
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| AbstractList | We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation. |
| Author | Du, Yuchuan Deng, Fuwen Shen, Yu Jin, Jiandong |
| Author_xml | – sequence: 1 givenname: Fuwen orcidid: 0000-0002-1150-682X surname: Deng fullname: Deng, Fuwen email: dengfw@sdtbu.edu.cn organization: Shandong Technology and Business University – sequence: 2 givenname: Jiandong surname: Jin fullname: Jin, Jiandong organization: Peking University – sequence: 3 givenname: Yu surname: Shen fullname: Shen, Yu organization: Tongji University – sequence: 4 givenname: Yuchuan surname: Du fullname: Du, Yuchuan organization: Tongji University |
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| SubjectTerms | adaptive control deep reinforcement learning ramp metering |
| Title | A dynamic self-improving ramp metering algorithm based on multi-agent deep reinforcement learning |
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