摘要
合理的航班协同离场前排序可以提高机场、航空公司、空管等部门的运行效率和可预测性,减少航班起飞前的等待时间。准确地预测航班撤轮挡时刻是建立航班起飞顺序的先决条件,对调整起飞前航班排序和计算航班起飞时间具有重要的决策意义。提出一个基于级联BP神经网络的航班撤轮挡时刻预测模型。该模型分别在航班过站过程的不同时刻进行航班撤轮挡时刻的预测,并进行过拟合研究。实验结果表明,与目前采用的经验统计预测模型相比,在相同时刻,该预测模型具有更高的预测准确率。
A reasonable arrangement of pre-departure sequence of flights can improve the efficiency and predictability of airport,airline and blank pipe,and reduce the waiting time before the aircrafts take off.Accurate prediction of the flight off-block time is a prerequisite for the establishment of a pre-departure sequence,which has important decision significance for adjusting the flight departure order and calculating the flight departure time.This paper proposed a flight off-block time prediction model based on cascaded BP neural network.The model predicted the flight off-block time at different times of the flight turnaround process,and made over-fitting study.The experimental results show that compared with the empirical statistical prediction model currently used,the model has higher prediction accuracy at the same time.
作者
徐涛
丁杨
卢敏
Xu Tao;Ding Yang;Lu Min(College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China;Information Technology Research Base of Civil Aviation Administration of China, Tianjin 300300, China;Key Laboratory of Intelligent Passenger Service of Civil Aviation, CAAC, Beijing 101318, China)
出处
《计算机应用与软件》
北大核心
2019年第6期226-232,共7页
Computer Applications and Software
基金
国家自然科学基金项目(61502499)
民航旅客服务智能化应用技术重点实验室项目
中央高校基本科研业务费科研专项(3122015Z007)
关键词
航班撤轮挡时刻预测
BP神经网络
级联模型
里程碑事件
过拟合
协同决策
Flight off-block time prediction
BP neural network
Cascaded model
Milestone event
Overfitting
Collaborative decision making
