BigARM : a big-data-driven airport resource management engine and application tools

Abstract

Resource management becomes a critical issue in airport operation since passenger throughput grows rapidly but the fixed resources such as baggage carousels hardly increase. We propose a Big-data-driven Airport Resource Management (BigARM) engine and develop a suite of application tools for efficient resource utilization and achieving customer service excellence. Specifically, we apply BigARM to manage baggage carousels, which balances the overload carousels and reduces the planning and rescheduling workload for operators. With big data analytic techniques, BigARM accurately predicts the flight arrival time with features extracted from cross-domain data. Together with a multi-variable reinforcement learning allocation algorithm, BigARM makes intelligent allocation decisions for achieving baggage load balance. We demonstrate BigARM in generating full-day initial allocation plans and recommendations for the dynamic allocation adjustments and verify its effectiveness.