离港航空器滑出时间的BP神经网络预测模型

准确地预测离港航空器滑出时间可有效提升机场场面运行效率，降低运行成本。构建基于BP 神经网络的离港航空器滑出时间预测模型，分析影响离港航空器滑出时间的可量化因素，并对其相关性进行检验；通过我国中南某机场2 周实际运行数据对模型进行验证，并以均方根误差、平均绝对误差和平均绝对误差百分比检验预测结果的准确性。结果表明：同时...

Taxi-out time prediction of departure aircraft based on BP neural network

In order to improve the operation efficiency of airport traffic, a prediction model of departure and departure time based on BP neural network was established. The key factors influencing the actual slip out time were analyzed, and the correlation was tested by SPSS. The model was verified by two weeks" actual operation data of an airport in central and southern China, and the root mean square error (RMSE), mean absolute error (MAE) and mean absolute error percentage (MAE) were analyzed. The results show that the new quantifiable factor, that is, the mean taxi time within one hour, has a significant effect on the actual taxi time. Although the time of the flight can also be quantified, the accuracy of the prediction is significantly reduced when this factor is added. The error between the prediction and the actual value was less than 5min, accounting for 84%.