离港航班滑出时间的SVM预测模型*
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中国民航飞行学院

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中图分类号:

V355.1

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


SVM prediction model of departure flights’ taxi-out time
Author:
Affiliation:

1.School of Air Traffic Control,Civil Aviation Flight University of China,Guang-han Sichuan 618307;2.China

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    为解决BP神经网络在离港航班滑出时间预测精度欠佳的问题,构建了基于支持向量机(Support Vector Mac, SVM)的离港航班滑出时间预测模型。首先,分析了影响离港航班滑出时间的可量化因素,构建了基于相关性分析的离港航班滑出时间预测模型;并对比分析了基于SVM和BP神经网络的滑出时间预测结果。结论表明:(1)离港航班滑出时间与同时段推出航班数量、同时段起飞航班数量、同时段落地航班数量、1小时平均滑出时间呈现强相关性,与滑行距离、转弯个数、延误时间相关但不显著,与起飞时刻所在时段不相关。(2)基于SVM和BP神经网络的预测结果趋势是一致的,考虑强相关和中度相关影响因素的七元组预测结果准确率达到最佳;引入不相关因素后模型的预测精度会下降。(3)基于SVM的滑出时间预测模型精度显著高于BP神经网络预测模型,滑出时间误差范围在内的预测准确率可达98%。

    Abstract:

    In order to solve the problem of poor prediction accuracy of departure flight time by BP neural network, a prediction model of departure flight time was established based on Support Vector Mac (SVM). Firstly, quantifiable factors affecting the departure time of flight are analyzed, and a prediction model of departure time of flight is built based on correlation analysis. The results of taxi-out time prediction based on SVM and BP neural network are compared and analyzed. The conclusions are as follows: (1) the taxi-out time of departing flights is strongly correlated with the number of launching flights in the same period, the number of taking off flights in the same period, the number of flights in the same period and the average taxiing time in one hour. It is correlated with taxiing distance, number of turns and delay time but not significantly, and is not correlated with the time period at the departure time. (2) The trend of prediction results based on SVM and BP neural network is consistent, and the accuracy of seven-tuple prediction results is the best considering strong correlation and moderate correlation factors. After introducing irrelevant factors, the prediction accuracy of the model will decrease. (3) The accuracy of the SVM-based taxi-out time prediction model is significantly higher than that of the BP neural network prediction model, and the prediction accuracy within the taxi-out time error range ±5 min can reach 98%.

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夏正洪,贾鑫磊. 离港航班滑出时间的SVM预测模型*[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-16
  • 最后修改日期:2022-03-30
  • 录用日期:2022-04-30
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