ST-GTNet: A Spatio-Temporal Graph Attention Network for Dynamic Airport Capacity Prediction
Sábháilte in:
| Foilsithe in: | Aerospace vol. 12, no. 9 (2025), p. 811-841 |
|---|---|
| Príomhchruthaitheoir: | |
| Rannpháirtithe: | , , , |
| Foilsithe / Cruthaithe: |
MDPI AG
|
| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3254460291 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2226-4310 | ||
| 024 | 7 | |a 10.3390/aerospace12090811 |2 doi | |
| 035 | |a 3254460291 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231330 |2 nlm | ||
| 100 | 1 | |a Qian Pinzheng |u Jiangsu Key Laboratory of Urban ITS, Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211189, China; 230228890@seu.edu.cn (P.Q.); 230229444@seu.edu.cn (H.Z.); lxhy919@seu.edu.cn (X.L.) | |
| 245 | 1 | |a ST-GTNet: A Spatio-Temporal Graph Attention Network for Dynamic Airport Capacity Prediction | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Dynamic evaluation of airport terminal capacity is critical for efficient operations, yet it remains challenging due to the complex interplay of spatial and temporal factors. Existing approaches often handle spatial connectivity and temporal fluctuations separately, limiting their predictive power under rapidly changing conditions. Here the ST-GTNet (Spatio-Temporal Graph Transformer Network) is presented, a novel deep learning model that integrates Graph Convolutional Networks (GCNs) with a Transformer architecture to simultaneously capture spatial interdependencies among airport gates and temporal patterns in operational data. To ensure interpretability and efficiency, a feature selection mechanism guided by XGBoost and SHAP (Shapley Additive Explanations) is incorporated to identify the most influential features. This unified spatio-temporal framework overcomes the limitations of conventional methods by learning spatial and temporal dynamics jointly, thereby enhancing the accuracy of dynamic capacity predictions. In a case study at a large international airport with a U-shaped corridor terminal, the ST-GTNet delivered robust and reliable capacity forecasts, validating its effectiveness in a complex real-world scenario. These findings highlight the potential of the ST-GTNet as a powerful tool for dynamic airport capacity evaluation and management. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Integer programming | ||
| 653 | |a Airport capacity | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Airline scheduling | ||
| 653 | |a Decision making | ||
| 653 | |a Airport terminals | ||
| 653 | |a Airports | ||
| 653 | |a Feature selection | ||
| 653 | |a Computer simulation | ||
| 653 | |a Civil aviation | ||
| 653 | |a Decision trees | ||
| 653 | |a Deep learning | ||
| 653 | |a Transportation terminals | ||
| 700 | 1 | |a Zhang, Jian |u Jiangsu Key Laboratory of Urban ITS, Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211189, China; 230228890@seu.edu.cn (P.Q.); 230229444@seu.edu.cn (H.Z.); lxhy919@seu.edu.cn (X.L.) | |
| 700 | 1 | |a Zhang, Haiyan |u Jiangsu Key Laboratory of Urban ITS, Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211189, China; 230228890@seu.edu.cn (P.Q.); 230229444@seu.edu.cn (H.Z.); lxhy919@seu.edu.cn (X.L.) | |
| 700 | 1 | |a Li Xunhao |u Jiangsu Key Laboratory of Urban ITS, Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing 211189, China; 230228890@seu.edu.cn (P.Q.); 230229444@seu.edu.cn (H.Z.); lxhy919@seu.edu.cn (X.L.) | |
| 700 | 1 | |a Ouyang Jie |u School of Transportation Science and Engineering, Civil Aviation University of China, Nanjing 211189, China; joyang@cauc.edu.cn | |
| 773 | 0 | |t Aerospace |g vol. 12, no. 9 (2025), p. 811-841 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254460291/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254460291/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254460291/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |