To improve the prediction accuracy of aircraft landing time, we propose a machine learning method to establish an approach time prediction model. Based on the actual operational situation, the primary reasons for different flying time in approach control are analyzed, including eight major factors with 17 important characteristics. Taking the aircraft flying time in approach airspace as labels, we use the proposed characteristics to build machine learning models with four popular machine learning algorithms:the ridge regression, random forest, support vector machine, and neural network. In the case of Nanjing Approach in China, four machine learning models are trained, validated and tested with practical operational data. The results show that the random forest based model exhibits the best prediction performance with good generalization ability, high accuracy and obvious regression effect. The initial arrival state of aircraft is the most important factor for approach time prediction, while the arrival point and initial altitude are two major characteristics for the prediction results.
YE Bojia
,
BAO Xu
,
LIU Bo
,
TIAN Yong
. Machine learning for aircraft approach time prediction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020
, 41(10)
: 324136
-324136
.
DOI: 10.7527/S1000-6893.2020.24136
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