A trajectory prediction model for commercial flight based on representative trajectory

Weifan Zhao; Qiang Tong; Jianzhi Cao; Yuli Chen; Xiulei Liu

doi:10.1117/12.2656766

20 October 2022 A trajectory prediction model for commercial flight based on representative trajectory

Weifan Zhao, Qiang Tong, Jianzhi Cao, Yuli Chen, Xiulei Liu

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124510I (2022) https://doi.org/10.1117/12.2656766
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

In commercial flight trajectory prediction, the models based on deep learning have high versatility and accurate forecast. However, these prediction models have the problem of poor effect when using small data sets. This paper proposes a commercial flight trajectory prediction model based on representative trajectory. This model consists of two parts: Representative trajectory generation and Trajectory prediction. Firstly, our model clusters the commercial flight trajectories on the same route, then generate representative trajectories to represent the route pattern. Secondly, our model uses the Savitzky-Golay filter to eliminate the noise characteristics in these representative trajectories, then combines those with a deviation correction algorithm to predict flight trajectories. By comparing with several other prediction models, the experiment results support that our model performs better in a small data set of the commercial flight trajectory.

Citation Download Citation

Weifan Zhao, Qiang Tong, Jianzhi Cao, Yuli Chen, and Xiulei Liu "A trajectory prediction model for commercial flight based on representative trajectory", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124510I (20 October 2022); https://doi.org/10.1117/12.2656766

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