Forecasting Aircraft Stand Snow Removal Capacity

Abstract

Winter weather can significantly disrupt airport operations, leading to flight delays, cancellations, and substantial economic losses. Proactively forecasting airport capacity during snowfall is essential to ensure timely flight cancellations and maintain operational continuity. While aircraft stand availability is a key determinant of overall airport capacity, data-driven insights into this process are lacking. This study addresses this gap by developing a forecasting model to predict aircraft stand snow removal capacity, using Amsterdam Airport Schiphol as a case study. A classification algorithm is employed to extract operational cleaning activities, cleaning, traveling, and idle periods, from historical radar data, enabling the assessment of current cleaning performance. Based on these insights, a simulation-based model was developed to forecast stand cleaning capacity under anticipated operational and environmental conditions. The model’s flexible design allows users to adjust scenario-specific input parameters, such as team availability and capacity restrictions, facilitating tailored scenario analysis. By simulating the alignment between available cleaning capacity and inbound flight schedules, the model identifies potential queuing situations and facilitates the assessment of additional capacity restrictions, supporting more informed capacity planning.