A Big-data-driven Airport Resource Management Engine and Application Tools.

We develop a series of prediction models using big data techniques to predict unknown factors affecting resource management, and design novel allocation algorithms to efficiently manage resources. We demonstrate our research achievements in baggage reclaim belt management at HKIA to efficiently balance overload reclaim belts for improving Custom Service Excellent. Specifically, we demonstrate the whole allocation process for any single flight selected by the user and showcase the principle and advantage of our intelligence prediction algorithms which outperform the accuracy of HKIA’s estimation in flight arrival time prediction and bag count prediction by 39.7% and 62.5% respectively. Meanwhile, we compare our full-day allocation plan to the one manually scheduled by experienced operators from HKIA to show the performance improvement in balancing overload reclaim belts.