Decentralized Multi-Agent Reinforcement Learning with Visible Light Communication for Robust Urban Traffic Signal Control

Manuel Augusto Vieira, Gonçalo Galvão, Manuela Vieira (), Mário Véstias, Paula Louro and Pedro Vieira
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Manuel Augusto Vieira: DEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal
Gonçalo Galvão: DEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal
Manuela Vieira: DEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal
Mário Véstias: DEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal
Paula Louro: DEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal
Pedro Vieira: DEETC-ISEL/IPL, R. Conselheiro Emídio Navarro, 1949-014 Lisboa, Portugal

Sustainability, 2025, vol. 17, issue 22, 1-32

Abstract: 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.

Keywords: sustainable urban mobility; intelligent traffic management; multi-agent reinforcement learning (MARL); deep reinforcement learning (DRL); visible light communication (VLC); energy efficiency; pedestrian safety; smart cities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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