Determining the Saturation Point for UAV Operations in Airport Environments: A Probabilistic Approach

The rapid integration of Uncrewed Aerial Vehicles (UAVs) into airport operations has led to a new era of aviation challenges, particularly in ensuring safety and maintaining operational efficiency. The current protocol for obtaining approval for UAV operations within airport zones necessitates obtaining clearances from both Air Traffic Control (ATC) and relevant government bodies. However, this process is hindered by a lack of uniform standards, particularly concerning the maximum number of UAVs allowed to operate simultaneously, leading to inconsistencies and potential operational challenges. The paper addresses these challenges, focusing on the current approval processes for UAV operations within airport environments. Our study introduces a novel approach to calculate the saturation point for UAV operations in and around airports. This saturation point is defined as the maximum safe limit of UAVs operating, without causing disruptions or safety hazards. To accurately determine this threshold, we consider a comprehensive range of factors, including air traffic classes, the volume of crewed aircraft traffic, prevailing weather conditions, and the capabilities of the UAVs themselves. Our analysis divides the issue into two main risk categories: hazards arising from pre-approved UAV activities and risks related to crewed aircraft. We then propose a probabilistic approach that assesses the frequency of crewed aircraft traffic in conjunction with diverse UAV operational scenarios, such as building inspections, to calculate the saturation point (P
s
). This model incorporates key parameters like geolocation, timing, and other operational metrics to predict potential mid-air collision (MAC) and near-mid-air collision (NMAC), as well as to ensure adequate clearance between aircraft. Furthermore, the paper outlines the essential operational parameters required from UAV operators and airport management to refine the accuracy of the saturation point calculation. To validate our approach, we use simulation supported by real flight data from Grand Forks International Airport, illustrating various scenarios and their impact on UAV operation safety. The outcome of this study is a probabilistic framework that provides an essential factor for air traffic control (ATC) and other relevant organizations to make decisions regarding the operation permission of UAV operation in and around airports. As a result, this research greatly contributes to the improvement of safe and efficient management of airspace in airport environments.