Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine Learning
Terminal airspace is the convergence area of air traffic flow, which is the bottleneck of air traffic management. With the rapid growth of air traffic volume, the impact of convective weather on flight operations is becoming more and more serious. To change the conditions and improve the utilization...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/3/288 |
| _version_ | 1827752014417756160 |
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| author | Shijin Wang Baotian Yang Rongrong Duan Jiahao Li |
| author_facet | Shijin Wang Baotian Yang Rongrong Duan Jiahao Li |
| author_sort | Shijin Wang |
| collection | DOAJ |
| description | Terminal airspace is the convergence area of air traffic flow, which is the bottleneck of air traffic management. With the rapid growth of air traffic volume, the impact of convective weather on flight operations is becoming more and more serious. To change the conditions and improve the utilization of terminal area airspace, a convective weather terminal area capacity (CWTAC) model is developed to quantify the effect of convective weather on the capacity of the terminal area in this paper. The airspace of the terminal area is divided into major airspace, minor airspace and no-impact airspace according to the distribution of the air traffic flow. Under convective weather, their permeabilities are calculated and used as input features, and the actual availability rate is set to label. Three machine learning algorithms, support vector regression (SVR), random forest (RF), and artificial neural network (ANN), are used to predict the availability rate. Then, the terminal airspace capacity under convective weather can be calculated. The historical operation data of the Guangzhou terminal area and the Wuhan terminal area are taken to test machine learning algorithms and verify the CWTAC model. It shows that all three machine learning algorithms are practical, and ANN is the best one based on mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The predicted capacity of the CWTAC model accords well with the actual flight number in the terminal airspace under convective weather. The reasons why they are not entirely consistent are also analyzed. |
| first_indexed | 2024-03-11T07:04:54Z |
| format | Article |
| id | doaj.art-b6692f8f6433418fbc2d547a9e67c548 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T07:04:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-b6692f8f6433418fbc2d547a9e67c5482023-11-17T08:59:00ZengMDPI AGAerospace2226-43102023-03-0110328810.3390/aerospace10030288Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine LearningShijin Wang0Baotian Yang1Rongrong Duan2Jiahao Li3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaZTE Corporation, Nanjing 211106, ChinaTerminal airspace is the convergence area of air traffic flow, which is the bottleneck of air traffic management. With the rapid growth of air traffic volume, the impact of convective weather on flight operations is becoming more and more serious. To change the conditions and improve the utilization of terminal area airspace, a convective weather terminal area capacity (CWTAC) model is developed to quantify the effect of convective weather on the capacity of the terminal area in this paper. The airspace of the terminal area is divided into major airspace, minor airspace and no-impact airspace according to the distribution of the air traffic flow. Under convective weather, their permeabilities are calculated and used as input features, and the actual availability rate is set to label. Three machine learning algorithms, support vector regression (SVR), random forest (RF), and artificial neural network (ANN), are used to predict the availability rate. Then, the terminal airspace capacity under convective weather can be calculated. The historical operation data of the Guangzhou terminal area and the Wuhan terminal area are taken to test machine learning algorithms and verify the CWTAC model. It shows that all three machine learning algorithms are practical, and ANN is the best one based on mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The predicted capacity of the CWTAC model accords well with the actual flight number in the terminal airspace under convective weather. The reasons why they are not entirely consistent are also analyzed.https://www.mdpi.com/2226-4310/10/3/288convective weatherterminal areaairspace capacityairspace availability ratemachine learningprediction |
| spellingShingle | Shijin Wang Baotian Yang Rongrong Duan Jiahao Li Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine Learning Aerospace convective weather terminal area airspace capacity airspace availability rate machine learning prediction |
| title | Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine Learning |
| title_full | Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine Learning |
| title_fullStr | Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine Learning |
| title_full_unstemmed | Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine Learning |
| title_short | Predicting the Airspace Capacity of Terminal Area under Convective Weather Using Machine Learning |
| title_sort | predicting the airspace capacity of terminal area under convective weather using machine learning |
| topic | convective weather terminal area airspace capacity airspace availability rate machine learning prediction |
| url | https://www.mdpi.com/2226-4310/10/3/288 |
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