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Artificial Intelligence (AI) integration in airport passenger boarding bridge systems improves operational efficiency and safety. AI systems automate tasks including bridge alignment, attachment, and monitoring. The systems use image recognition and machine learning algorithms to optimize docking procedures, increasing accuracy and reducing errors. AI also enables predictive maintenance through analysis of operational data and sensor inputs to detect potential equipment issues early, reducing maintenance downtime.
For instance, Kansai Airports implemented an AI-powered automated system at Osaka International Airport (ITAMI) for connecting boarding bridges to aircraft. The system uses image recognition technology to attach bridges through a single-touch operation, reducing human error and improving operational safety. This automation enhances the passenger boarding process and overall travel experience.
The airport passenger boarding bridge market shows increased adoption of customized and modular passenger boarding bridge (PBB) designs that accommodate different aircraft types and terminal configurations. Airports require flexible systems that can serve wide-body, narrow-body, and regional aircraft with minimal adjustments. The modular designs reduce installation time and minimize operational disruptions during upgrades. Large terminals are implementing multi-bridge configurations to improve boarding and deplaning efficiency. Enhanced passenger comfort and safety requirements drive the integration of automated alignment systems, weather-resistant structures, and ergonomic designs.