Variable speed limit (VSL) control is a promising traffic management strategy
for enhancing safety and mobility. This work introduces MARVEL, a multi-agent
reinforcement learning (MARL) framework for implementing large-scale VSL
control on freeway corridors using only commonly available data. The agents
learn through a reward structure that incorporates adaptability to traffic
conditions, safety, and mobility; enabling coordination among the agents. The
proposed framework scales to cover corridors with many gantries thanks to a
parameter sharing among all VSL agents. The agents are trained in a
microsimulation environment based on a short freeway stretch with 8 gantries
spanning 7 miles and tested with 34 gantries spanning 17 miles of I-24 near
Nashville, TN. MARVEL improves traffic safety by 63.4% compared to the no
control scenario and enhances traffic mobility by 14.6% compared to a
state-of-the-practice algorithm that has been deployed on I-24. An
explainability analysis is undertaken to explore the learned policy under
different traffic conditions and the results provide insights into the
decision-making process of agents. Finally, we test the policy learned from the
simulation-based experiments on real input data from I-24 to illustrate the
potential deployment capability of the learned policy.

变速限制、交通管理、多智能体强化学习、高速公路走廊和交通安全是本研究的主要关键词和研究领域，并通过使用常见数据引入了一个名为 MARVEL 的多智能体强化学习框架，以实现大规模变速限制控制。