Research on Flight Intelligent Sorting Algorithm in AI-based Air Traffic Control Systems

Flight delay is a significant challenge faced by air traffic control systems, severely affecting passenger travel experience and airline operational efficiency. This paper addresses this issue by proposing an intelligent flight sorting method based on machine learning and deep learning. By preprocessing and feature engineering of flight historical data, airport data, and route data, a set of high-quality feature sets is constructed. On this basis, models such as random forests and LSTM are employed to predict flight delays and intelligently sort flights according to the prediction results. Experimental results demonstrate that this method significantly enhances the accuracy and efficiency of flight sorting, reducing the impact of flight delays on passenger travel. This study provides a novel and effective approach to addressing flight delay problems, offering broad application prospects and practical value.