4D Trajectory Prediction of Continuous Descent Operation in Congested Terminal Control Area

WANG Chao; CHEN Hanlu; QIN Hongkun; LIU Bo

doi:10.3969/j.issn.0258-2724.20230380

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Journal of Southwest Jiaotong University > 2025 >

WANG Chao, CHEN Hanlu, QIN Hongkun, LIU Bo. 4D Trajectory Prediction of Continuous Descent Operation in Congested Terminal Control Area[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230380

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WANG Chao, CHEN Hanlu, QIN Hongkun, LIU Bo. 4D Trajectory Prediction of Continuous Descent Operation in Congested Terminal Control Area[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230380

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4D Trajectory Prediction of Continuous Descent Operation in Congested Terminal Control Area

doi: 10.3969/j.issn.0258-2724.20230380

College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

Received Date: 10 Aug 2023
Rev Recd Date: 29 Nov 2023

Available Online: 21 Jan 2025

Abstract

Abstract

In order to implement continuous descent operation (CDO) in a congested terminal control area and estimate its CO₂ emission reduction benefits, a 4D trajectory prediction method for CDO based on data drive and optimal control theory was proposed. Firstly, the affinity propagation trajectory clustering method was employed to recognize typical horizontal arrival routes. According to typical horizontal arrival routes, a multi-phase optimal control model for CDO in vertical profiles was established, with the objectives of minimizing time and fuel consumption. Additionally, a novel solving method, namely genetic algorithm-based CDO (GACDO) for optimal control model was proposed. Finally, 4D trajectory prediction and emission reduction benefit comparison experiments in typical horizontal arrival route identification and CDO modes were conducted by using actual trajectory data in the terminal control area. The experimental results show that ideal 4D trajectories for CDO can be achieved. With the minimum time as the optimization objective, the average operation time and CO₂ emission are reduced by 26% and 8%, respectively. With the minimum fuel consumption as the optimization objective, the operation time and CO₂ emission are decreased by 17% and 20%, respectively.
- 4D trajectory prediction,
- continuous descent operation,
- genetic algorithm,
- trajectory clustering,
- optimal control

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[1]	SÁEZ R, PRATS X, POLISHCHUK T, et al. Traffic synchronization in terminal airspace to enable continuous descent operations in trombone sequencing and merging procedures: an implementation study for Frankfurt Airport[J]. Transportation Research Part C: Emerging Technologies, 2020, 121: 102875.1-102875.23. doi: 10.1016/j.trc.2020.102875
[2]	ZENG W L, CHU X, XU Z F, et al. Aircraft 4D trajectory prediction in civil aviation: a review[J]. Aerospace, 2022, 9(2): 91.1-91.19. doi: 10.3390/aerospace9020091
[3]	王超,郭九霞,沈志鹏. 基于基本飞行模型的4D航迹预测方法[J]. 西南交通大学学报,2009,44(2): 295-300. doi: 10.3969/j.issn.0258-2724.2009.02.028 WANG Chao, GUO Jiuxia, SHEN Zhipeng. Prediction of 4D trajectory based on basic flight models[J]. Journal of Southwest Jiaotong University, 2009, 44(2): 295-300. doi: 10.3969/j.issn.0258-2724.2009.02.028
[4]	张军峰,蒋海行,武晓光,等. 基于BADA及航空器意图的四维航迹预测[J]. 西南交通大学学报,2014,49(3): 553-558. doi: 10.3969/j.issn.0258-2724.2014.03.028 ZHANG Junfeng, JIANG Haixing, WU Xiaoguang, et al. 4 D trajectory prediction based on BADA and aircraft intent[J]. Journal of Southwest Jiaotong University, 2014, 49(3): 553-558. doi: 10.3969/j.issn.0258-2724.2014.03.028
[5]	王莉莉,刘鑫宇. 基于自主改航的交叉航班流预先冲突解脱研究[J/OL]. 西南交通大学学报,1-9[2024-12-05]. http://kns.cnki.net/kcms/detail/51.1277.U.20240322.1643.006.html. WANG Lili, Liu xinyu, A study on pre-conflict resolution of cross-flight streams based on autonomous re-routing[J/OL] Journal of Southwest Jiaotong University, 1-9[2024-12-05]. http://kns.cnki.net/kcms/detail/51.1277.U.20240322.1643.006.html.
[6]	ZHANG J F, LIU J, HU R, et al. Online four dimensional trajectory prediction method based on aircraft intent updating[J]. Aerospace Science and Technology, 2018, 77: 774-787. doi: 10.1016/j.ast.2018.03.037
[7]	FRANCO A, RIVAS D, VALENZUELA A. Probabilistic aircraft trajectory prediction in cruise flight considering ensemble wind forecasts[J]. Aerospace Science and Technology, 2018, 82/83: 350-362. doi: 10.1016/j.ast.2018.09.020
[8]	DANCILA R I, BOTEZ R M. New atmospheric data model for constant altitude accelerated flight performance prediction calculations and flight trajectory optimization algorithms[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2021, 235(4): 405-426. doi: 10.1177/0954410020945555
[9]	ZENG W L, QUAN Z B, ZHAO Z Y, et al. A deep learning approach for aircraft trajectory prediction in terminal airspace[J]. IEEE Access, 2020, 8: 151250-151266. doi: 10.1109/ACCESS.2020.3016289
[10]	WU X P, YANG H Y, CHEN H, et al. Long-term 4D trajectory prediction using generative adversarial networks[J]. Transportation Research Part C: Emerging Technologies, 2022, 136: 103554.1-103554.13. doi: 10.1016/j.trc.2022.103554
[11]	MA L, TIAN S. A hybrid CNN-LSTM model for aircraft 4D trajectory prediction[J]. IEEE Access, 2020, 8: 134668-134680. doi: 10.1109/ACCESS.2020.3010963
[12]	DHIEF I, WANG Z, LIANG M, et al. Predicting aircraft landing time in extended-TMA using machine learning methods[C]//Proceedings of the 9th International Conference for Research in Air Transportation (ICRAT). Florida: [s.n.], 2020: 23-26.
[13]	ROCHA MURCA M C, DE OLIVEIRA M. A data-driven probabilistic trajectory model for predicting and simulating terminal airspace operations[C]//2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). San Antonio: IEEE, 2020: 1-7.
[14]	PARK S G, CLARKE J P. Optimal control based vertical trajectory determination for continuous descent arrival procedures[J]. Journal of Aircraft, 2015, 52(5): 1469-1480. doi: 10.2514/1.C032967
[15]	MA L B, TIAN Y G, ZHANG Y, et al. Trajectory optimization of aircraft for a continuous descent continuous procedure[C]//2020 Chinese Automation Congress (CAC). Shanghai: IEEE, 2020: 2063-2067.
[16]	GONZÁLEZ-ARRIBAS D, SOLER M, SANJURJO-RIVO M. Robust aircraft trajectory planning under wind uncertainty using optimal control[J]. Journal of Guidance, Control, and Dynamics, 2017, 41(3): 673-688.
[17]	ZENG W L, XU Z F, CAI Z P, et al. Aircraft trajectory clustering in terminal airspace based on deep autoencoder and Gaussian mixture model[J]. Aerospace, 2021, 8(9): 266.1-266.18. doi: 10.3390/aerospace8090266
[18]	FREY B J, DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814): 972-976. doi: 10.1126/science.1136800
[19]	NUIC A. User manual for the base of aircraft data (BADA) revision 3.6[EB/OL]. (2004-10-14)[2023-05-05]. https://www.eurocontrol.int/publication/user-manual-base-aircraft-data-bada.