Prediction of Departure Aircraft Taxi Time Based on Deep Learning

基于深度学习的离场航空器滑行时间预测

With the continuous increase in the number of flights,the use of airport collaborative decision-making(ACDM)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 blockout.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.

随着航班数量的不断增加,机场协同决策系统(Airport collaborative decision?making,A?CDM)的使用也越来越广泛。滑行时间预测的准确性对A?CDM计算离场航空器起飞排序队列和给出准确的撤轮挡时间具有重要的作用。本文提出一种基于时间?空间?环境数据的深度学习模型(Spatio?temporal?environment deep learning model,STEDL)来提高滑行时间预测的准确性。该模型由时间?流量变量(机场实际容量,场面航空器数量,时间段)、空间变量(滑行距离)、外部环境变量(天气,流控信息,跑道运行模式,机型)3部分组成。使用STEDL模型对香港机场离场航空器滑行时间进行预测验证。实验结果显示,STEDL模型预测准确率为95.4%,预测精度明显优于其他机器学习算法。