Aircraft arrival and departure time interval prediction based on improved Bayesian classifier

Juzan Xie; Bo Yin; Zhaoyang Lu

doi:10.1117/12.2686974

10 August 2023 Aircraft arrival and departure time interval prediction based on improved Bayesian classifier

Juzan Xie, Bo Yin, Zhaoyang Lu

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592W (2023) https://doi.org/10.1117/12.2686974
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

The main purpose of this paper is to improve the accuracy of the prediction of aircraft delay time. Using K-Medoids clustering algorithm to cluster the time intervals between arrival and departure time based on historical data, and then use the principal component analysis to reduce the dimensionality of the data. Finally, The Bayesian Classifier is used for classification processing to predict the category of the time interval to which the data belongs. The improved Bayesian Classifier has greatly improved accuracy and reduced errors, then we can use this data to predict the actual departure time of the aircraft. It is convenient for us to predict the delay time of the aircraft, and the prediction result is more accurate.

Citation Download Citation

Juzan Xie, Bo Yin, and Zhaoyang Lu "Aircraft arrival and departure time interval prediction based on improved Bayesian classifier", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592W (10 August 2023); https://doi.org/10.1117/12.2686974

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