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.


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    Title :

    A Multi-Agent Rollout Approach for Highway Bottleneck Decongestion in Mixed Autonomy


    Contributors:
    Liu, Lu (author) / Wang, Maonan (author) / Pun, Man-On (author) / Xiong, Xi (author)


    Publication date :

    2024-09-24


    Size :

    643456 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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