Enhancing airport traffic management: innovative integration of visible light communication and artificial intelligence

Airports are complex environments with terminals, gates, shops, and facilities hosting assets like luggage carts, maintenance tools, and ground vehicles. In these spaces, pedestrians and Autonomous Guided Vehicles (AGVs) require precise indoor localization for efficient navigation. Real-time localization reduces confusion, saves time, and enhances passenger experiences by providing clear directions to gates, check-ins, baggage claims, and lounges. This study proposes an AI and Visible Light Communication (VLC)-based airport management system to optimize traffic, reduce congestion, and improve safety. VLC-enabled luminaires serve as transmission points, offering location-specific guidance, while AI agents track and manage assets in real time. Tetrachromatic LED luminaires with On-Off Keying (OOK) modulation and SiC optical receivers replace traditional gateways, forming a mesh hybrid network for reliable data exchange. AI agents use deep reinforcement learning (DRL) to process data, optimize routes, and prioritize movements. Traffic states are encoded as inputs to neural networks trained via Q-learning. Results show improved traffic control, travel direction inference, and route optimization through agent-based simulations. This approach enhances indoor navigation without GPS, ensuring smooth operations for AGVs and pedestrians. Integrating AI and VLC improves airport efficiency, safety, and passenger satisfaction.