MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits

arXiv:2310.12359v2 Announce Type: replace-cross
Abstract: 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 …

abstract adjusting agent algorithms arxiv control cs.lg cs.ma deployment management marvel multi-agent real-time reinforcement safety scale speed strategy traffic traffic management traffic safety type