Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods
This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6309272 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568538049544192 |
|---|---|
| author | Zhao Yang Yifan Wang Jie Li Liming Liu Jiyang Ma Yi Zhong |
| author_facet | Zhao Yang Yifan Wang Jie Li Liming Liu Jiyang Ma Yi Zhong |
| author_sort | Zhao Yang |
| collection | DOAJ |
| description | This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models. |
| format | Article |
| id | doaj-art-f3bdf39c70094963a18f76f032b96568 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f3bdf39c70094963a18f76f032b965682025-02-03T00:58:51ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/63092726309272Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning MethodsZhao Yang0Yifan Wang1Jie Li2Liming Liu3Jiyang Ma4Yi Zhong5National Key Laboratory of Air Traffic Flow Management, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No. 29, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No. 29, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No. 29, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No. 29, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No. 29, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No. 29, Nanjing 211106, ChinaThis study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models.http://dx.doi.org/10.1155/2020/6309272 |
| spellingShingle | Zhao Yang Yifan Wang Jie Li Liming Liu Jiyang Ma Yi Zhong Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods Complexity |
| title | Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods |
| title_full | Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods |
| title_fullStr | Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods |
| title_full_unstemmed | Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods |
| title_short | Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods |
| title_sort | airport arrival flow prediction considering meteorological factors based on deep learning methods |
| url | http://dx.doi.org/10.1155/2020/6309272 |
| work_keys_str_mv | AT zhaoyang airportarrivalflowpredictionconsideringmeteorologicalfactorsbasedondeeplearningmethods AT yifanwang airportarrivalflowpredictionconsideringmeteorologicalfactorsbasedondeeplearningmethods AT jieli airportarrivalflowpredictionconsideringmeteorologicalfactorsbasedondeeplearningmethods AT limingliu airportarrivalflowpredictionconsideringmeteorologicalfactorsbasedondeeplearningmethods AT jiyangma airportarrivalflowpredictionconsideringmeteorologicalfactorsbasedondeeplearningmethods AT yizhong airportarrivalflowpredictionconsideringmeteorologicalfactorsbasedondeeplearningmethods |