Shanghai Hongqiao International Airport (SHA) is enhancing the passenger experience and operational efficiency with Artificial Intelligence (AI) powered solution coupled with Machine Learning (ML) algorithms from ADB SAFEGATE.
While enhancing its previously-installed Airport Operational Database/Resource Management System (AODB/RMS), ADB SAFEGATE has built a new prediction model applying advanced AI/ML techniques and data analytics, which will help calculate an aircraft’s ETA and pre-allocate flight stands. It is designed to reduce unreasonable stand adjustments and improve the airbridge usage rate, lowering overall security risks caused by flight conflicts.
“Thanks to ADB SAFEGATE’s AI prediction algorithm, 75% of our flights now land within 30 minutes of their scheduled landing time,” said Mr. Wang Zhi, Section Manager of SHA ITC. “This improved accuracy has directly benefited our pre-allocation of stands, reducing the need to make stand changes by 25%. This has decreased security risks caused by flight conflicts, as well as allowed more passengers to avail airbridges rather than wait for buses.”
Peng Guan, Vice President, China at ADB SAFEGATE, commented: “As an in-house vendor serving SHA for more than 15 years, we understand the operational challenges at SHA. With operating pressure increasing, airports must make full use of limited resources. We have built an evaluation mechanism of ground operation efficiency through intelligent data analysis that has improved the allocation of stand resources, resulting in the capability to manage, and even accommodate, more flights. Digital technologies are playing a key role in optimising the operational efficiency of airports and we hope to continue to build long-term partnerships with customers in the region to support their transformation on this path.”
