ISBN: 978-981-14-4787-7 DOI: 10.18178/wcse.2020.06.013
Departure Aircraft Taxi Time Prediction Based on Gradient Boosting Regression Tree
Abstract— The rough estimation of aircraft taxi-out time causes the waste of runway slot and the low utilization efficiency. Therefore, it is urgent to propose a precise taxi time estimation method. Using the current operational data of the airport, combined with data mining to find potential factors affecting aircraft taxiing time. First, considering the large data dimensions, complexity of the algorithm and prediction inaccuracy caused by redundant learning samples, a feature selection model for optimal set of feature variables is established based on Gradient Boosted Tree (GBT). Then, the taxi-out time is predicted based on Gradient Boosting Regression Tree (GBRT). Finally, the simulation which takes Shanghai Pudong Airport as an example shows that the prediction accuracy is 76.29% within±3min , and up to 94.31% within±5min , which verifies that our method has better performance on prediction accuracy than other regression methods.
Index Terms—Intelligent Traffic; Taxi Time Prediction; Gradient Boosted Tree; Departure Aircraft; Taxi-out Delay
Mingxin Gu, Chaoyang Lu, Meize Dai, Jingjing Qian
Civil Aviation College of Nanjing University of Aeronautics and Astronautics, CHINA
Cite: Mingxin Gu, Chaoyang Lu, Meize Dai, Jingjing Qian , " Departure Aircraft Taxi Time Prediction Based on Gradient Boosting Regression Tree " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 76-80, Shanghai, China, 19-21 June, 2020.