A Multi-Agent Rollout Approach for Highway Bottleneck Decongenston in Mixed Autonomy
<|reference_start|>A Multi-Agent Rollout Approach for Highway Bottleneck Decongenston in Mixed Autonomy: The integration of autonomous vehicles (AVs) into the existing transportation infrastructure offers a promising solution to alleviate congestion and enhance mobility. This research explores a novel approach to traffic optimization by employing a multi-agent rollout approach within a mixed autonomy environment. The study concentrates on coordinating the speed of human-driven vehicles by longitudinally controlling AVs, aiming to dynamically optimize traffic flow and alleviate congestion at highway bottlenecks in real-time. We model the problem as a decentralized partially observable Markov decision process (Dec-POMDP) and propose an improved multi-agent rollout algorithm. By employing agent-by-agent policy iterations, our approach implicitly considers cooperation among multiple agents and seamlessly adapts to complex scenarios where the number of agents dynamically varies. Validated in a real-world network with varying AV penetration rates and traffic flow, the simulations demonstrate that the multi-agent rollout algorithm significantly enhances performance, reducing average travel time on bottleneck segments by 9.42% with a 10% AV penetration rate.<|reference_end|>
@article{liu2024a,
    title={A Multi-Agent Rollout Approach for Highway Bottleneck Decongenston in
  Mixed Autonomy},
    author={Lu Liu, Maonan Wang, Man-On Pun and Xi Xiong},
    journal={arXiv preprint arXiv:2405.03132},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03132},
    primaryClass={cs.MA}
}