  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.
