Authors:
Bálint Pelenczei
1
;
István Knáb
1
;
Bálint Kővári
2
;
3
;
Tamás Bécsi
2
and
László Palkovics
1
;
4
Affiliations:
1
Systems and Control Laboratory, HUN-REN Institute for Computer Science and Control (SZTAKI), Budapest, Hungary
;
2
Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, Hungary
;
3
Asura Technologies Ltd., Budapest, Hungary
;
4
Széchenyi István University, Győr, Hungary
Keyword(s):
Reinforcement Learning, Variable Speed Limit Control, Intelligent Transportation Systems, Cooperative Traffic Control, Multi-Agent Systems.
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 generali
zability, 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.
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