Flight trajectory time and altitude prediction based on support vector and decision tree regressions

Yingchao Xiao; Yuanyuan Ma; Qiucheng Xu; Hui Ding

doi:10.1117/12.2574419

24 June 2020 Flight trajectory time and altitude prediction based on support vector and decision tree regressions

Yingchao Xiao, Yuanyuan Ma, Qiucheng Xu, Hui Ding

Proceedings Volume 11526, Fifth International Workshop on Pattern Recognition; 1152608 (2020) https://doi.org/10.1117/12.2574419
Event: Fifth International Workshop on Pattern Recognition, 2020, Chengdu, China

Abstract

Four dimensional (4D) flight trajectories play an important role in air traffic future plans. In this paper, the time and altitude variables in 4D trajectories are analyzed for their characteristics, and the procedure of preprocessing flight trajectory data is provided, and support vector regression and decision tree regression are introduced to build the prediction models for trajectory time and altitude, respectively. It is demonstrated by the experiments on actual flight trajectory data that the proposed method can improve the 4D trajectory prediction accuracy effectively.

Citation Download Citation

Yingchao Xiao, Yuanyuan Ma, Qiucheng Xu, and Hui Ding "Flight trajectory time and altitude prediction based on support vector and decision tree regressions", Proc. SPIE 11526, Fifth International Workshop on Pattern Recognition, 1152608 (24 June 2020); https://doi.org/10.1117/12.2574419

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