Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble
Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affect...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/6400839 |
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| _version_ | 1832565896615297024 |
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| author | Xi Zhu Ke Zhang Zhuxi Zhang Lifei Tan |
| author_facet | Xi Zhu Ke Zhang Zhuxi Zhang Lifei Tan |
| author_sort | Xi Zhu |
| collection | DOAJ |
| description | Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affected by convective weather, making accurately predicting trajectories challenging. To address this problem, we propose a boosted spatiotemporal deep learning ensemble for mining the law of how convective weather affects flight trajectory stretching. Instead of conventionally representing trajectory data in a geographic coordinate system, we design a relative coordinate system for gaining new trajectory features which tangibly reflect trajectory’s relations with planned route and convective weather. Besides, we raise a boosted ensemble framework of spatiotemporal deep learning models, trained by the samples pairing sequential trajectory with graphical weather, dedicating to strengthen the mining of the high-value training samples that involve explicit flight deviations caused by convective weather. The experiments using actual flight and weather data demonstrate our method’s superiority in predicting flight trajectory affected by convective weather. |
| format | Article |
| id | doaj-art-f2895f4dd4654b81b944d17d2c4d804a |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-f2895f4dd4654b81b944d17d2c4d804a2025-02-03T01:06:29ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/6400839Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning EnsembleXi Zhu0Ke Zhang1Zhuxi Zhang2Lifei Tan3School of Electronic and Information EngineeringSchool of Electronic and Information EngineeringNational Engineering Laboratory for Comprehensive Transportation Big Data Application TechnologyGeneral Logistics Information CenterFlight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affected by convective weather, making accurately predicting trajectories challenging. To address this problem, we propose a boosted spatiotemporal deep learning ensemble for mining the law of how convective weather affects flight trajectory stretching. Instead of conventionally representing trajectory data in a geographic coordinate system, we design a relative coordinate system for gaining new trajectory features which tangibly reflect trajectory’s relations with planned route and convective weather. Besides, we raise a boosted ensemble framework of spatiotemporal deep learning models, trained by the samples pairing sequential trajectory with graphical weather, dedicating to strengthen the mining of the high-value training samples that involve explicit flight deviations caused by convective weather. The experiments using actual flight and weather data demonstrate our method’s superiority in predicting flight trajectory affected by convective weather.http://dx.doi.org/10.1155/2024/6400839 |
| spellingShingle | Xi Zhu Ke Zhang Zhuxi Zhang Lifei Tan Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble Journal of Advanced Transportation |
| title | Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble |
| title_full | Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble |
| title_fullStr | Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble |
| title_full_unstemmed | Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble |
| title_short | Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble |
| title_sort | predicting flight trajectory in convective weather through boosted spatiotemporal deep learning ensemble |
| url | http://dx.doi.org/10.1155/2024/6400839 |
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