南京航空航天大学学报（英文版）2020，Vol.37Issue(2)：232-241,10.
基于深度学习的离场航空器滑行时间预测
Prediction of Departure Aircraft Taxi Time Based on Deep Learning
摘要
Abstract
With the continuous increase in the number of flights,the use of airport collaborative decision?making(A?CDM)systems has been more and more widely spread. The accuracy of the taxi time prediction has an important effect on the A?CDM calculation of the departure aircraft's take?off queue and the accurate time for the aircraft block?out. The spatial?temporal?environment deep learning(STEDL)model is presented to improve the prediction accuracy of departure aircraft taxi?out time. The model is composed of time?flow sub?model (airport capacity,number of taxiing aircraft,and different time periods),spatial sub?model (taxiing distance) and environmental sub?model (weather,air traffic control,runway configuration,and aircraft category). The STEDL model is used to predict the taxi time of departure aircraft at Hong Kong Airport and the results show that the STEDL method has a prediction accuracy of 95.4％. The proposed model also greatly reduces the prediction error rate compared with the other machine learning methods.关键词
航空运输/滑行时间/深度学习/场面运行/卷积神经网络Key words
air transportation/taxi time/deep learning/surface movement/convolutional neural network(CNN)分类
交通工程引用本文复制引用
李楠,焦庆宇,朱新华,王少聪..基于深度学习的离场航空器滑行时间预测[J].南京航空航天大学学报（英文版）,2020,37(2):232-241,10.基金项目
This work was supported by the Na?tional Natural Science Foundation of China(Nos.U1833103, 71801215) （Nos.U1833103, 71801215）
the China Civil Aviation Environment and Sus?tainable Development Research Center Open Fund(No.CES?CA2019Y04). （No.CES?CA2019Y04）
