Enhancing Airport Traffic Flow

Abstract

Highlights: What are the main findings? The AI and VLC-based airport traffic management system enhances traffic flow efficiency, reduces congestion, and enhances safety through real-time asset tracking, adaptive reward mechanisms, and intelligent rerouting strategies. VLC-enabled infrastructure: Utilizes tetrachromatic LED luminaires and SiC optical receivers in a hybrid mesh network to provide precise, location-specific guidance and replace traditional gateway systems. What is the implication of the main finding? AI-driven optimization: Employs Deep Reinforcement Learning (DRL) with Q-learning to support adaptive reward allocation, dynamic phase control, and intelligent rerouting across multiple intersections. GPS-free indoor localization: Enables seamless navigation for AGVs and pedestrians through VLC-based geolocation, contributing to smoother operations, improved mobility, and an enhanced passenger experience. Airports are complex environments where efficient localization and intelligent traffic management are essential for ensuring smooth navigation and operational efficiency for both pedestrians and Autonomous Guided Vehicles (AGVs). This study presents an Artificial Intelligence (AI)-driven airport traffic management system that integrates Visible Light Communication (VLC), rerouting techniques, and adaptive reward mechanisms to optimize traffic flow, reduce congestion, and enhance safety. VLC-enabled luminaires serve as transmission points for location-specific guidance, forming a hybrid mesh network based on tetrachromatic LEDs with On-Off Keying (OOK) modulation and SiC optical receivers. AI agents, driven by Deep Reinforcement Learning (DRL), continuously analyze traffic conditions, apply adaptive rewards to improve decision-making, and dynamically reroute agents to balance traffic loads and avoid bottlenecks. Traffic states are encoded and processed through Q-learning algorithms, enabling intelligent phase activation and responsive control strategies. Simulation results confirm that the proposed system enables more balanced green time allocation, with reductions of up to 43% in vehicle-prioritized phases (e.g., Phase 1 at C1) to accommodate pedestrian flows. These adjustments lead to improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian traffic across multiple intersections. Additionally, traffic flow responsiveness is preserved, with critical clearance phases maintaining stability or showing slight increases despite pedestrian prioritization. Simulation results confirm improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian flows. The system also enables accurate indoor localization without relying on a Global Positioning System (GPS), supporting seamless movement and operational optimization. By combining VLC, adaptive AI models, and rerouting strategies, the proposed approach contributes to safer, more efficient, and human-centered airport mobility.