A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction
Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6638130 |
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| _version_ | 1832560059757887488 |
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| author | Weili Zeng Juan Li Zhibin Quan Xiaobo Lu |
| author_facet | Weili Zeng Juan Li Zhibin Quan Xiaobo Lu |
| author_sort | Weili Zeng |
| collection | DOAJ |
| description | Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network to construct airports’ relationship. For adjacent airports, weights of edges are measured by the spherical distance between them, while the number of flight pairs between them is utilized for airports connected by flights. On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). To verify the model’s effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. The experimental results suggest that the proposed method is superior to the existing mainstream methods in terms of accuracy and robustness. |
| format | Article |
| id | doaj-art-bb8c7bd079c84abdb5b3ba3baa5a1257 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-bb8c7bd079c84abdb5b3ba3baa5a12572025-02-03T01:28:30ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66381306638130A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay PredictionWeili Zeng0Juan Li1Zhibin Quan2Xiaobo Lu3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaDepartment of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, ChinaSchool of Automation, Southeast University, Nanjing 210096, ChinaDue to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network to construct airports’ relationship. For adjacent airports, weights of edges are measured by the spherical distance between them, while the number of flight pairs between them is utilized for airports connected by flights. On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). To verify the model’s effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. The experimental results suggest that the proposed method is superior to the existing mainstream methods in terms of accuracy and robustness.http://dx.doi.org/10.1155/2021/6638130 |
| spellingShingle | Weili Zeng Juan Li Zhibin Quan Xiaobo Lu A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction Journal of Advanced Transportation |
| title | A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction |
| title_full | A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction |
| title_fullStr | A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction |
| title_full_unstemmed | A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction |
| title_short | A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction |
| title_sort | deep graph embedded lstm neural network approach for airport delay prediction |
| url | http://dx.doi.org/10.1155/2021/6638130 |
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