Research on assessment of air traffic control operation quality based on track data

Abstract

Abstract Evaluating the quality of air traffic control operations is crucial for enhancing airspace management. Thus, this paper proposes a data mining approach for conducting a comprehensive assessment of control operation quality (COQ) in increasingly complex operation environments. First, the authors establish a COQ evaluation index system that combines both subjective and objective measures. Key index parameters are determined using wavelet filtering and interval estimation techniques on the basis of data mining results. Second, the authors apply an entropy‐weighted cloud model to label data samples and classify COQ into ‘excellent’, ‘good’, and ‘fair’ levels. Finally, the authors establish an support vector machine‐based COQ assessment model using XGBoost feature combinations to verify the practical feasibility and scientific validity of their approach.

Keywords

• control operation quality
• entropy‐weighted cloud model
• feature combination
• support vector machine (SVM)
• XGBoost