Construction and Validation of Big Data Analytics in Air Traffic Control Flow Prediction Models

Abstract: Air traffic control flow prediction is one of the key issues in aviation transportation management, crucial for optimizing resource allocation, enhancing control efficiency, and ensuring flight operation safety. This paper proposes a solution for air traffic control flow prediction based on big data analysis and machine learning techniques. Through extensive feature engineering and model construction using large-scale historical data, multiple prediction models including Support Vector Regression, Random Forest, Gradient Boosting Decision Tree, and an ensemble model were trained. Comprehensive performance evaluations using cross-validation and multiple metrics were conducted. Experimental results demonstrate that the proposed ensemble model effectively combines the strengths of multiple base models, significantly outperforming individual models in terms of prediction accuracy, stability, and generalization ability. This provides an efficient and reliable technical path for air traffic control flow prediction, with broad application prospects.