MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914716663676928 |
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| author | Zhang, Yuhang Quinones-Grueiro, Marcos Zhang, Zhiyao Wang, Yanbing Barbour, William Biswas, Gautam Work, Daniel |
| author_facet | Zhang, Yuhang Quinones-Grueiro, Marcos Zhang, Zhiyao Wang, Yanbing Barbour, William Biswas, Gautam Work, Daniel |
| contents | Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic conditions. Most of the deployed VSL control algorithms so far are rule-based, lacking generalizability under varying and complex traffic scenarios. In this work, we propose MARVEL (Multi-Agent Reinforcement-learning for large-scale Variable spEed Limits), a novel framework for large-scale VSL control on highway corridors with real-world deployment settings. MARVEL utilizes only sensing information observable in the real world as state input and learns through a reward structure that incorporates adaptability to traffic conditions, safety, and mobility, thereby enabling multi-agent coordination. With parameter sharing among all VSL agents, the proposed framework scales to cover corridors with many agents. The policies are trained in a microscopic traffic simulation environment, focusing on a short freeway stretch with 8 VSL agents spanning 7 miles. For testing, these policies are applied to a more extensive network with 34 VSL agents spanning 17 miles of I-24 near Nashville, TN, USA. MARVEL-based method improves traffic safety by 63.4% compared to the no control scenario and enhances traffic mobility by 58.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. Besides, we conduct an explainability analysis to examine the decision-making process of the agents and explore the learned policy under different traffic conditions. Finally, we test the response of the policy learned from the simulation-based experiments with real-world data collected from I-24 and illustrate its deployment capability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_12359 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits Zhang, Yuhang Quinones-Grueiro, Marcos Zhang, Zhiyao Wang, Yanbing Barbour, William Biswas, Gautam Work, Daniel Multiagent Systems Machine Learning Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic conditions. Most of the deployed VSL control algorithms so far are rule-based, lacking generalizability under varying and complex traffic scenarios. In this work, we propose MARVEL (Multi-Agent Reinforcement-learning for large-scale Variable spEed Limits), a novel framework for large-scale VSL control on highway corridors with real-world deployment settings. MARVEL utilizes only sensing information observable in the real world as state input and learns through a reward structure that incorporates adaptability to traffic conditions, safety, and mobility, thereby enabling multi-agent coordination. With parameter sharing among all VSL agents, the proposed framework scales to cover corridors with many agents. The policies are trained in a microscopic traffic simulation environment, focusing on a short freeway stretch with 8 VSL agents spanning 7 miles. For testing, these policies are applied to a more extensive network with 34 VSL agents spanning 17 miles of I-24 near Nashville, TN, USA. MARVEL-based method improves traffic safety by 63.4% compared to the no control scenario and enhances traffic mobility by 58.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. Besides, we conduct an explainability analysis to examine the decision-making process of the agents and explore the learned policy under different traffic conditions. Finally, we test the response of the policy learned from the simulation-based experiments with real-world data collected from I-24 and illustrate its deployment capability. |
| title | MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits |
| topic | Multiagent Systems Machine Learning |
| url | https://arxiv.org/abs/2310.12359 |