Integration of Visible Light Communication and Deep Reinforcement Learning to Enhance Urban Traffic Management

This study integrates Visible Light Communication (VLC) and a Deep Reinforcement Learning (DRL) system to enhance traffic signal control, reduce congestion, and improve safety through real-time data-driven management. VLC leverages existing infrastructure to transmit real-time data on vehicle and pedestrian dynamics, while trained agents optimize traffic signals and vehicle trajectories across intersections. A centralized system trains a unified DRL model to coordinate local agents managing individual intersections, enabling real-time signal adjustments via a queue/request/response methodology. Simulations and real-world trials validate the approach, showing significant reductions in waiting and travel times, particularly under rerouting scenarios. Scalable to diverse intersection types, the system adapts dynamically to changing traffic conditions, improving efficiency and safety.