4D Trajectory Prediction Based On Data Analysis And Ensemble Learning

4D trajectory prediction is the core element of future air transportation system,which is intended to improve the operational ability and the predictability of air traffic.At the same time,it is the basis of conflict detection and resolution,aircraft sequencing and separation management.It is also seen as an efficient way to solve worldwide air traffic management problems.In this thesis,we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Maneuvering Area(TMA).The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network(MCNN)-based prediction.Firstly,in the preprocessing part,after data cleaning,filtering and data re-sampling,we applied principal Component Analysis(PCA)to reduce the dimension of trajectory vector variable.Then,the trajectories are split into several clusters by clustering algorithm.Using nested cross validation,MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival(ETA)for each individual cluster cell.Finally,the predicted ETA for each new flight is generated in different cluster cells classified by decision trees.To assess the performance of MCNN model,the Multiple Linear Regression(MLR)model is proposed as the comparison learning model,and K-means and DBSCAN are proposed as two comparison clustering models in preprocessing part.With real 4D trajectory data in Beijing TMA,experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model,both in trajectory clustering and short-term 4D trajectory prediction.In addition,it can make an accurate trajectory prediction in terms of Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)with regards to comparison models.