Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival Time
To enhance the accuracy and real-time capability of estimated in-block time (EIBT) predictions at airports, this study proposes a two-stage integrated prediction method. By extending the prediction time window for arrival times, this method systematically models and analyzes the integrated arrival t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/3/250 |
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| author | Xiaowei Tang Mengfan Ye Jiaqi Wu Shengrun Zhang |
| author_facet | Xiaowei Tang Mengfan Ye Jiaqi Wu Shengrun Zhang |
| author_sort | Xiaowei Tang |
| collection | DOAJ |
| description | To enhance the accuracy and real-time capability of estimated in-block time (EIBT) predictions at airports, this study proposes a two-stage integrated prediction method. By extending the prediction time window for arrival times, this method systematically models and analyzes the integrated arrival time, thereby achieving precise EIBT predictions. This study divides the arrival process into the approach flight stage and the taxi-in stage, constructing predictive models for each and identifying key influencing factors. Additionally, copula entropy is employed to optimize feature selection. Based on operational data from Shanghai Pudong International Airport, a LightGBM-based prediction model was developed and validated across multiple datasets. The results demonstrate that the two-stage integrated forecasting method significantly outperforms single-stage modeling, with the best model achieving a prediction accuracy of 87.11% within a ±5 min error margin. Furthermore, this study validates the effectiveness of copula entropy in enhancing model prediction performance. This research provides theoretical support and practical references for improving the real-time predictive capabilities of airport collaborative decision-making systems, as well as a technical pathway for integrated air-surface management research at multi-runway airports. |
| format | Article |
| id | doaj-art-b67a2660cc4c4d21955bea3dd1e6cf8f |
| institution | DOAJ |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-b67a2660cc4c4d21955bea3dd1e6cf8f2025-08-20T02:41:51ZengMDPI AGAerospace2226-43102025-03-0112325010.3390/aerospace12030250Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival TimeXiaowei Tang0Mengfan Ye1Jiaqi Wu2Shengrun Zhang3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaTo enhance the accuracy and real-time capability of estimated in-block time (EIBT) predictions at airports, this study proposes a two-stage integrated prediction method. By extending the prediction time window for arrival times, this method systematically models and analyzes the integrated arrival time, thereby achieving precise EIBT predictions. This study divides the arrival process into the approach flight stage and the taxi-in stage, constructing predictive models for each and identifying key influencing factors. Additionally, copula entropy is employed to optimize feature selection. Based on operational data from Shanghai Pudong International Airport, a LightGBM-based prediction model was developed and validated across multiple datasets. The results demonstrate that the two-stage integrated forecasting method significantly outperforms single-stage modeling, with the best model achieving a prediction accuracy of 87.11% within a ±5 min error margin. Furthermore, this study validates the effectiveness of copula entropy in enhancing model prediction performance. This research provides theoretical support and practical references for improving the real-time predictive capabilities of airport collaborative decision-making systems, as well as a technical pathway for integrated air-surface management research at multi-runway airports.https://www.mdpi.com/2226-4310/12/3/250air transportationintegrated arrival time predictiontaxi-in timeapproach flight timeairport surface operationsmachine learning |
| spellingShingle | Xiaowei Tang Mengfan Ye Jiaqi Wu Shengrun Zhang Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival Time Aerospace air transportation integrated arrival time prediction taxi-in time approach flight time airport surface operations machine learning |
| title | Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival Time |
| title_full | Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival Time |
| title_fullStr | Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival Time |
| title_full_unstemmed | Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival Time |
| title_short | Two Stages of Arrival Aircraft: Influencing Factors and Prediction of Integrated Arrival Time |
| title_sort | two stages of arrival aircraft influencing factors and prediction of integrated arrival time |
| topic | air transportation integrated arrival time prediction taxi-in time approach flight time airport surface operations machine learning |
| url | https://www.mdpi.com/2226-4310/12/3/250 |
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