A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty Handling
General aviation trajectory prediction plays a crucial role in enhancing safety and operational efficiency at non-towered airports. However, current research faces multiple challenges including variable weather conditions, complex aircraft interactions, and flight pattern constraints specified by ge...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6810 |
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| author | Houru Hu Ye Yuan Qingwen Xue |
| author_facet | Houru Hu Ye Yuan Qingwen Xue |
| author_sort | Houru Hu |
| collection | DOAJ |
| description | General aviation trajectory prediction plays a crucial role in enhancing safety and operational efficiency at non-towered airports. However, current research faces multiple challenges including variable weather conditions, complex aircraft interactions, and flight pattern constraints specified by general aviation regulations. This paper proposes a deep learning method based on stochastic processes aimed at addressing uncertainty issues in general aviation trajectory prediction. First, we design a probabilistic encoder–decoder structure enabling the model to output trajectory distributions rather than single paths, with regularization terms based on Lyapunov stability theory to ensure predicted trajectories maintain stable convergence while satisfying flight patterns. Second, we develop a multi-layer attention mechanism that accounts for weather factors, enhancing the model’s responsiveness to environmental changes. Validation using the TrajAir dataset from Pittsburgh-Butler Regional Airport (KBTP) not only advances deep learning applications in general aviation but also provides new insights for solving trajectory prediction problems. |
| format | Article |
| id | doaj-art-e8e3bdabbb4f48a7bac5e2842c8369d1 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e8e3bdabbb4f48a7bac5e2842c8369d12025-08-20T03:32:31ZengMDPI AGApplied Sciences2076-34172025-06-011512681010.3390/app15126810A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty HandlingHouru Hu0Ye Yuan1Qingwen Xue2The College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaThe College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaThe College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaGeneral aviation trajectory prediction plays a crucial role in enhancing safety and operational efficiency at non-towered airports. However, current research faces multiple challenges including variable weather conditions, complex aircraft interactions, and flight pattern constraints specified by general aviation regulations. This paper proposes a deep learning method based on stochastic processes aimed at addressing uncertainty issues in general aviation trajectory prediction. First, we design a probabilistic encoder–decoder structure enabling the model to output trajectory distributions rather than single paths, with regularization terms based on Lyapunov stability theory to ensure predicted trajectories maintain stable convergence while satisfying flight patterns. Second, we develop a multi-layer attention mechanism that accounts for weather factors, enhancing the model’s responsiveness to environmental changes. Validation using the TrajAir dataset from Pittsburgh-Butler Regional Airport (KBTP) not only advances deep learning applications in general aviation but also provides new insights for solving trajectory prediction problems.https://www.mdpi.com/2076-3417/15/12/6810trajectory predictiondeep learninggeneral aviation |
| spellingShingle | Houru Hu Ye Yuan Qingwen Xue A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty Handling Applied Sciences trajectory prediction deep learning general aviation |
| title | A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty Handling |
| title_full | A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty Handling |
| title_fullStr | A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty Handling |
| title_full_unstemmed | A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty Handling |
| title_short | A Deep Learning Approach for General Aviation Trajectory Prediction Based on Stochastic Processes for Uncertainty Handling |
| title_sort | deep learning approach for general aviation trajectory prediction based on stochastic processes for uncertainty handling |
| topic | trajectory prediction deep learning general aviation |
| url | https://www.mdpi.com/2076-3417/15/12/6810 |
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