This study integrates Visible Light Communication (VLC) and Artificial Intelligence (AI) 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 AI agents employing Deep Reinforcement Learning (DRL) 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. Furthermore, the integration of VLC underscores the importance of cybersecurity in smart city infrastructures. This research aligns with CyberSecPro’s mission (https://www.cybersecpro-project.eu/), advancing cybersecurity competencies and ensuring privacy-respecting innovations in traffic management systems. The integration of VLC technology for real-time data transmission not only optimizes traffic flow but also highlights the importance of cybersecurity in smart city infrastructures ensuring secure and privacy-respecting innovations in traffic management systems.
Innovative integration of visible light communication and artificial intelligence to enhance urban traffic management
AI and Optical Data Sciences VI ; 2025 ; San Francisco, California, United States
Proc. SPIE ; 13375
2025-03-21
Conference paper
Electronic Resource
English
Enhancing Urban Traffic Management with Visible Light Communication and Reinforcement Learning
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