Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Mech. Eng.

Sec. Mechatronics

Volume 11 - 2025 | doi: 10.3389/fmech.2025.1650918

This article is part of the Research TopicApplications of Artificial Intelligence and IoT Technologies in Smart Manufacturing Vol. 2View all 3 articles

MULTI-AGENT REINFORCEMENT LEARNING FRAMEWORK FOR AUTONOMOUS TRAFFIC SIGNAL CONTROL IN SMART CITIES

Provisionally accepted
Olamide  OlusanyaOlamide OlusanyaYetunde  OwoshoYetunde OwoshoILESANMI  DaniyanILESANMI Daniyan*Adedayo  W ElegbedeAdedayo W ElegbedeQueen  B SodipoQueen B Sodipo
  • Bells University of Technology, Ota, Nigeria

The final, formatted version of the article will be published soon.

The increasing urbanization across the world necessitate efficient traffic management especially in the emerging economies. This paper presents an intelligent framework aimed at enhancing traffic signal management within complex road networks through the creation and evaluation of a multi-agent reinforcement learning (MARL) framework. The research explored how Reinforcement Learning (RL) algorithms can be employed to optimize the flow of traffic, lessen bottleneck, and enhance overall transportation safety and efficiency. Additionally, the research explored the design and simulation of a typical traffic environment that is, an intersection, defined and implemented a Multi-Agent System (MAS), and developed a Multi-Agent reinforcement learning model for traffic management within a simulated environment this model leverages actor- -–critics and deep Q Network (DQN) strategies for learning and coordination, and performed the evaluation of the MARL model. Novel approaches for decentralized decision-making and dynamic resource allocation were developed to enable real-time adaptation to changing traffic conditions and emergent situations. Performance evaluation using metrics such as waiting time, queue length, and congestion were carried out in the SUMO simulation platforms (Simulation of Urban Mobility) to evaluate the efficiency of the proposed solution in various traffic scenarios. The outcome of the simulation conducted in this study showed an improvement in queue management and traffic flow by 64.5% and 70.0% respecitively with improvement in performance of the proposed model over the episodes. The results show that the RL model policy showed better performance compared to the baseline policy, indicating that the model learned over different episodes. The results also show that the MARL-based approach performs better for decentralized traffic control systems in both scalability and adaptability. The proposed solution supports real-time decision-making, reduces traffic congestion, and improves the efficiency of the urban transportation system.

Keywords: Smart Cities, multi-agent systems, reinforcement learning, Traffic signal control, Intelligent Transportation Systems, SUMO simulation

Received: 24 Jun 2025; Accepted: 01 Sep 2025.

Copyright: © 2025 Olusanya, Owosho, Daniyan, Elegbede and Sodipo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: ILESANMI Daniyan, Bells University of Technology, Ota, Nigeria

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.