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...

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Main Authors: Zhao Yang, Yifan Wang, Jie Li, Liming Liu, Jiyang Ma, Yi Zhong
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/6309272
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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