Learning Real Trajectory Data to Enhance Conflict Detection Accuracy in Closest Point of Approach

Abstract

Closest Point of Approach (CPA) is one of the main problems in aircraft Conflict Detection (CD). It aims to find out the minimum distance and the associated time between two aircraft on the same altitude with crossing traffic. Conventional CPA prediction model generally assumes that the speed and heading of the aircraft are constant. But the uncertainties in real operations lead to the inaccuracy of CPA prediction. In this paper, we introduce a novel CD framework with Machine Learning (ML) methods. It aims to improve the CPA prediction accuracy with the help of real trajectory data. The new model contributes to not only reduce the number of fault short-mid term conflict alert for air traffic controllers but also support the implementation of future free flight concept, so as to reduce fuel consumption and emission. In our study, we firstly propose a data processing method to generate a close-to-reality simulation data from Mode-S observations. Then, feature engineering is used to transform the raw data into suitable features, which will enable the ML models to make predictions with high- performance. Six prevailing ML methods (MLR, SVM, FFNNs, KNN, GBM, RF) are used to predict the CPA time and distance. Their prediction results are compared with the conventional CPA model (baseline). The simulation results demonstrate that the GBM is the best prediction model both in CPA prediction and conflict detection. However, the results also prove that not all ML models outperform the baseline CPA model. Suitable ML methods can greatly enhance the accuracy of conflict detection. Refereed/Peer-reviewed