Adaptive Highway Traffic Management: A Reinforcement Learning Approach for Variable Speed Limit Control with Random Anomalies

Bálint Pelenczei, István Knáb, Bálint Kővári, Bálint Kővári, Tamás Bécsi, László Palkovics, László Palkovics

2024

Abstract

Efficient traffic flow management on highway scenarios is crucial for ensuring safety and minimizing emissions through the reduction of so-called shockwave effects. In this paper, we propose a novel approach based on cooperative Multi Agent Reinforcement Learning for optimizing traffic flow, utilizing Variable Speed Limit Control in dynamic simulation environments with random anomalies. Our method leverages Reinforcement Learning to adaptively adjust speed limits on distinct road sections in response to alternating traffic conditions, thereby improving not only general traffic flow parameters, but also reducing sustainability measures overall. Through extensive simulations in a Simulation of Urban MObility environment, we demonstrate the superiority of our approach in enhancing traffic flow efficiency and robustness compared to alternative solutions found in literature. Our findings reveal an enhanced performance of RL-based VSL control over traditional approaches due to its generalizability, which contributes to the progression of Intelligent Transportation Systems by presenting a proactive and adaptable resolution for highway traffic management within dynamic real-world contexts.

Paper Citation

in Harvard Style

Pelenczei B., Knáb I., Kővári B., Bécsi T. and Palkovics L. (2024). Adaptive Highway Traffic Management: A Reinforcement Learning Approach for Variable Speed Limit Control with Random Anomalies. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 117-124. DOI: 10.5220/0012920700003822

in Bibtex Style

@conference{icinco24,
author={Bálint Pelenczei and István Knáb and Bálint Kővári and Tamás Bécsi and László Palkovics},
title={Adaptive Highway Traffic Management: A Reinforcement Learning Approach for Variable Speed Limit Control with Random Anomalies},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2024},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012920700003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Adaptive Highway Traffic Management: A Reinforcement Learning Approach for Variable Speed Limit Control with Random Anomalies
SN - 978-989-758-717-7
AU - Pelenczei B.
AU - Knáb I.
AU - Kővári B.
AU - Bécsi T.
AU - Palkovics L.
PY - 2024
SP - 117
EP - 124
DO - 10.5220/0012920700003822
PB - SciTePress