Air traffic complexity evaluation with novel complexity features and mRMR-XGBoost

Abstract

Air traffic complexity evaluation is a critical problem in air traffic system operation, especially for air traffic safety and air traffic controller deployment. Many researches focus on using mathematical modeling or machine learning methods to evaluate air traffic complexity. However, there are still challenges in accurate evaluation, which is affected by lack of effective complexity related features and complicated feature-complexity relationship. Based on the existing complexity features, this paper proposes additional domain features, time-dependent features and data distribution features to construct a novel air traffic complexity feature set. A mRMR method for feature selection is then applied to filter out redundant features. Finally, the filtered feature set and corresponding air traffic complexity level are input into XGBoost model for learning the relationship, so as to achieve high-performance evaluation of air traffic complexity in the face of new air traffic data. The experimental results show that the proposed features are beneficial to the evaluation of air traffic complexity, and the XGBoost model with mRMR method can effectively select the important features and mine the relationships within air traffic complexity data, resulting in an improvement in overall evaluation performance by at least 5% while using less than half of the original number of features.