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This paper introduces an integrated approach to address challenges in traffic monitoring and control, alongside traffic simulation, by leveraging Visible Light Communication (VLC) technology. The proposed method optimizes traffic light signals and vehicle and pedestrians trajectories at urban intersections, incorporating Vehicle-to-Vehicle (V2V), Vehicle-to- Infrastructure (V2I), Infrastructures-to-Vehicles (I2V), and Pedestrians-to-Infrastructures (P2I) VLC communication. Experimental results demonstrate the feasibility of implementing these VLC modes in adaptive traffic control systems. Through modulated light, information exchange occurs between connected vehicles (CVs) and infrastructure elements like streetlamps and traffic light signals. Cooperative CVs share position and speed data via V2V communication within control zones, enabling adaptability to various traffic movements during signal phases. By utilizing Reinforcement Learning and the Simulation of Urban Mobility (SUMO) agent-based simulator, optimal traffic light control policies are determined. Unlike conventional methods focused solely on maximizing traffic capacity, this approach integrates traffic efficiency and safety considerations, including pedestrian concerns at intersections. Simulation scenarios adapted from real-world environments, such as Lisbon, feature interconnected intersections with traffic flow impact. A deep reinforcement learning algorithm dynamically manages traffic flows during peak hours via V2V and V/P2I communications, while prioritizing pedestrian and vehicle waiting times. VLC mechanisms facilitate queue/request/response interactions. A comparative analysis highlight the proposed approach's benefits in throughput, delay reduction, and minimizing vehicle stops, revealing improved patterns for signal and trajectory optimization. Evaluation on separate training and test sets ensures model reliability and effectiveness.
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