Aircraft Trajectory Prediction Based on Bayesian Optimised Temporal Convolutional Network–Bidirectional Gated Recurrent Unit Hybrid Neural Network
Efficient and accurate flight trajectory prediction is a key technology for promoting intelligent and informative air traffic management and improving the operational capabilities and predictability of air traffic. To address the problems in extracting hidden information from historical trajectory i...
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| Published in: | International Journal of Aerospace Engineering Vol. 2022; pp. 1 - 19 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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New York
Hindawi
15.12.2022
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1687-5966, 1687-5974 |
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| Abstract | Efficient and accurate flight trajectory prediction is a key technology for promoting intelligent and informative air traffic management and improving the operational capabilities and predictability of air traffic. To address the problems in extracting hidden information from historical trajectory information, the approach must accurately select high-dimensional features related to the prediction target and overcome the short-term memory of the time series. Herein, we present a novel trajectory prediction model based on a dual-self-attentive (DSA)-temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU) neural network. In this model, the TCN provides highly stable training, high parallelism, and a flexible perceptual domain. The self-attentive mechanism of the TCN structure can focus on features that contribute the most to the output. After the TCN, the BiGRU network combined with the self-attentive mechanism is used to further bidirectionally mine the connections between the features and outputs of the trajectory sequence, and a Bayesian algorithm is used to optimise the hyperparameters of the model for optimal performance. A comparison and validation based on current well-known neural network models (i.e., CNN, TCN, GRU, and their variants) shows that the DSA-TCN-BiGRU model based on Bayesian hyperparameter optimisation has the best performance. Therefore, the improved predictive model is applicable and valuable, providing a basis for future decision trajectory-based operations. |
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| AbstractList | Efficient and accurate flight trajectory prediction is a key technology for promoting intelligent and informative air traffic management and improving the operational capabilities and predictability of air traffic. To address the problems in extracting hidden information from historical trajectory information, the approach must accurately select high-dimensional features related to the prediction target and overcome the short-term memory of the time series. Herein, we present a novel trajectory prediction model based on a dual-self-attentive (DSA)-temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU) neural network. In this model, the TCN provides highly stable training, high parallelism, and a flexible perceptual domain. The self-attentive mechanism of the TCN structure can focus on features that contribute the most to the output. After the TCN, the BiGRU network combined with the self-attentive mechanism is used to further bidirectionally mine the connections between the features and outputs of the trajectory sequence, and a Bayesian algorithm is used to optimise the hyperparameters of the model for optimal performance. A comparison and validation based on current well-known neural network models (i.e., CNN, TCN, GRU, and their variants) shows that the DSA-TCN-BiGRU model based on Bayesian hyperparameter optimisation has the best performance. Therefore, the improved predictive model is applicable and valuable, providing a basis for future decision trajectory-based operations. |
| Audience | Academic |
| Author | Ding, Weijie Huang, Jin |
| Author_xml | – sequence: 1 givenname: Jin surname: Huang fullname: Huang, Jin organization: Air Traffic Management CollegeCivil Aviation Flight University of ChinaGuanghanSichuan 618307Chinacafuc.edu.cn – sequence: 2 givenname: Weijie orcidid: 0000-0002-1748-035X surname: Ding fullname: Ding, Weijie organization: Air Traffic Management CollegeCivil Aviation Flight University of ChinaGuanghanSichuan 618307Chinacafuc.edu.cn |
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| Cites_doi | 10.1109/ACCESS.2022.3149231 10.1080/15567036.2022.2053250 10.1016/j.adhoc.2021.102476 10.1016/j.neucom.2022.06.014 10.1016/j.trc.2022.103554 10.1007/s40747-021-00606-4 10.1016/j.trc.2022.103878 10.1007/s12652-021-03497-y 10.1016/j.trc.2021.103326 10.1109/ACCESS.2021.3126117 10.1007/978-3-030-59016-1_30 10.3390/aerospace8090266 10.1109/TITS.2021.3095129 10.3390/w12113032 10.3390/en15155584 10.1007/s12530-020-09345-2 10.19651/j.cnki.emt.2208843 10.1016/j.trc.2021.103331 10.1109/ACCESS.2020.3010963 10.26599/TST.2020.9010057 10.3390/aerospace9080464 10.1007/s10489-022-03309-6 10.1016/j.paerosci.2020.100640 10.1007/s10489-022-03292-y 10.13700/j.bh.1001-5965.2022.0446 10.3390/su14073862 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Jin Huang and Weijie Ding. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Jin Huang and Weijie Ding. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| DOI | 10.1155/2022/2086904 |
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| References | 22 24 25 26 27 28 C. Morales (11) 29 H. Zhang (21) 2022; 35 Y. A. Pan (8) H. Wang (33) Z. Fan (5) 30 31 L. Ma (9) 2019; 37 32 A. Zhang (16) 2022; 52 12 34 G. Zazzaro (10) Y. Ma (13) 2016 35 14 36 15 37 17 39 18 K. Yang (2) Z. C. Li (6) 2015 19 Y. Pang (3) 2022; 249 1 4 7 M. Munem (38) H. Zhang (23) 2020; 42 20 |
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| SubjectTerms | Accuracy Aerospace engineering Air traffic control Air traffic management Aircraft Algorithms Analysis Artificial neural networks Automation Bayesian analysis Civil aviation Cluster analysis Clustering Cost control Data mining Deep learning Efficiency Evaluation Flying-machines Kinematics Neural networks Optimization Prediction models R&D Research & development Surveillance Technology application Time series Trajectories |
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| Title | Aircraft Trajectory Prediction Based on Bayesian Optimised Temporal Convolutional Network–Bidirectional Gated Recurrent Unit Hybrid Neural Network |
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