Publicação
Intelligent traffic intersection management through multi-agent reinforcement learning
| Resumo: | Abstract Urban traffic management remains a persistent challenge for modern cities, particularly during peak hours when large volumes of vehicles and pedestrians converge, causing severe congestion, delays, and increased road safety risks. Given the limited feasibility of expanding physical infrastructure, it becomes essential to explore intelligent and adaptive solutions for traffic signal control. This work focuses on the application of Multi-Agent Reinforcement Learning (MARL) algorithms to optimize decision-making and coordination of traffic signals in urban networks. It is assumed that Visible Light Communication (VLC) between vehicles and infrastructure is available to provide real-time data required for the decision process, although this component is not the primary focus of the research. To validate the proposed approach, a simulation environment was developed using Simulation of Urban Mobility (SUMO), consisting of five interconnected signalized intersections. Within this context, different MARL algorithms were studied and compared, including Deep Q-Learning Network (DQN) and Multi-Agent Proximal Policy Optimization (MAPPO), with the objective of evaluating their performance under heterogeneous and dynamic traffic scenarios. The results show that MAPPO consistently outperforms DQN-based methods, achieving faster and more complete clearance of vehicles while maintaining lower waiting times for pedestrians. QT-DQN provides slight improvements over DQN in vehicle flow but at the cost of harming pedestrian performance. Overall, the study demonstrates that MARL methods, and particularly MAPPO, offer significant improvements in traffic efficiency and fairness, reinforcing their potential for deployment in real-world urban environments. |
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| Autores principais: | Antunes, Tomás Alexandre Henriques |
| Assunto: | Multi-agent reinforcement learning Visible light communication Artificial intelligence Traffic signal control Urban traffic control Road safety Aprendizagem por reforço multiagente Comunicação por luz visível Inteligência artificial Controlo da sinalização semafórica Gestão de tráfego urbano Segurança rodoviária |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Lisboa |
| Resumo: | Abstract Urban traffic management remains a persistent challenge for modern cities, particularly during peak hours when large volumes of vehicles and pedestrians converge, causing severe congestion, delays, and increased road safety risks. Given the limited feasibility of expanding physical infrastructure, it becomes essential to explore intelligent and adaptive solutions for traffic signal control. This work focuses on the application of Multi-Agent Reinforcement Learning (MARL) algorithms to optimize decision-making and coordination of traffic signals in urban networks. It is assumed that Visible Light Communication (VLC) between vehicles and infrastructure is available to provide real-time data required for the decision process, although this component is not the primary focus of the research. To validate the proposed approach, a simulation environment was developed using Simulation of Urban Mobility (SUMO), consisting of five interconnected signalized intersections. Within this context, different MARL algorithms were studied and compared, including Deep Q-Learning Network (DQN) and Multi-Agent Proximal Policy Optimization (MAPPO), with the objective of evaluating their performance under heterogeneous and dynamic traffic scenarios. The results show that MAPPO consistently outperforms DQN-based methods, achieving faster and more complete clearance of vehicles while maintaining lower waiting times for pedestrians. QT-DQN provides slight improvements over DQN in vehicle flow but at the cost of harming pedestrian performance. Overall, the study demonstrates that MARL methods, and particularly MAPPO, offer significant improvements in traffic efficiency and fairness, reinforcing their potential for deployment in real-world urban environments. |
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