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Publication
Decentralized multi-agent reinforcement learning with visible light communication for robust urban traffic signal control
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Advisor(s)
Abstract(s)
The rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management.
Description
Keywords
Sustainable urban mobility Intellingent traffic management Multi-agent reinforcement learning (MARL) Deep reinforcement learning (DRL) Visible light communication (VLC) Energy efficiency Pedestrian safety Smart cities
Pedagogical Context
Citation
Vieira, M. A., Galvão, G., Vieira, M., Véstias, M., Louro, P., & Vieira, P. (2025). Decentralized multi-agent reinforcement learning with visible light communication for robust urban traffic signal control. Sustainability, 17(22), 10056. https://doi.org/10.3390/su172210056
Publisher
MDPI AG