Identification of Airport Similar Days Based on K-prototype Cluster and Grey Correlation Analysis

Abstract Considering similar air traffic control techniques for the present based on close historical dates is a good approach due to unpredictability of weather and traffic, as well increase controller efficiency. A K-prototype clustering technique grey correlation analysis are proposed discover days address problem identification. Firstly, datasets used create set features broken down into numerical categorical attributes. Secondly, data clustered using clustering, which then paired with identify reference day examine management initiatives employed that day. Finally, research uses actual information aircraft schedules from Nanjing Lukou International Airport examples. The outcomes demonstrate picked by model representative can serve foundation airport controllers' decision-making.