ACTA AERONAUTICAET ASTRONAUTICA SINICA >
FTCA-Transformer: A method for predicting flight off-block time
Received date: 2025-06-03
Revised date: 2025-09-04
Accepted date: 2025-10-11
Online published: 2025-10-30
Supported by
Key Project of Civil Aviation Joint Fund of National Natural Science Foundation of China(U2333206);Natural Science Foundation of Tianjin City(25JCQNJC00250);Open Funding from Provincial-Ministerial Research Institutions of Civil Aviation University of China(CAITAIT-202405)
Accurate prediction of the Actual Off-Block Time （AOBT） is a crucial component for optimizing airport operational efficiency and enhancing the performance of Airport Collaborative Decision-Making （A-CDM） systems. To address the challenges posed by complex spatiotemporal dependencies and Gibbs phenomena in time-series variations， this study proposes a hybrid prediction model-FTCA-Transformer-integrating a Frequency-enhanced Channel Attention mechanism with a Temporal Convolutional Network （TCN） and Transformer architecture. First， milestone events in ground handling operations are encoded into unified sequential data， enabling the extraction of hidden temporal and seasonal features across timestamps. Next， the TCN captures local temporal segment information， while the multi-head self-attention mechanism in the Transformer extends temporal correlations from individual points to segment-level dependencies， overcoming the limitations of recurrent neural networks in modeling long-range dependencies. Furthermore， a Frequency-enhanced Channel Attention mechanism based on Discrete Cosine Transform （DCT） is introduced to effectively exploit frequency-domain features of ground operation data while mitigating Gibbs phenomena associated with conventional frequency transforms. Finally， experimental results using real ground handling operation data from a major hub airport in North China demonstrate that， under single-step prediction， the proposed FTCA-Transformer outperforms baseline methods， reducing the Mean Absolute Error （MAE）， Mean Absolute Percentage Error （MAPE）， and Root Mean Square Error （RMSE） by 48.0%， 53.5% and 44.7%， respectively. Ablation studies further confirm the positive contributions of each module within the model.
Yongkang ZHAO , Zuolong LIU , Xia FENG . FTCA-Transformer: A method for predicting flight off-block time[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(5) : 332357 -332357 . DOI: 10.7527/S1000-6893.2025.32357
| [1] | 中国民用航空局. 2024年全国民用运输机场生产统计公报［EB/OL］. 民航资源网. （2025-03-14）［2025-06-03］.. |
| Civil Aviation Administration of China. 2024 national civil transport airport production statistics bulletin ［EB/OL］. CARNOC. （2025-03-14）［2025-06-03］. （in Chinese）. | |
| [2] | 中国民用航空局. 2023年民航行业发展统计公报［EB/OL］. 民航资源网. （2024-05-31）［2025-06-03］. . |
| Civil Aviation Administration of China. 2023 statistical bulletin on the development of the civil aviation industry［EB/OL］.CARNOC.（2024-05-31）［2025-05-12］. （in Chinese）. | |
| [3] | 中国民用航空局. 机场协同决策系统技术规范［EB/OL］.民航资源网. （2022-02-18）［2025-05-12］ . |
| Civil Aviation Administration of China. Technical specifications for airport collaborative decision making systems［EB/OL］. CARNOC.（2022-02-18）［2025-05-12］. （in Chinese）. | |
| [4] | IP W H， WAN D， CHO V. Aircraft ground service scheduling problems and their genetic algorithm with hybrid assignment and sequence encoding scheme［J］. IEEE Systems Journal， 2013， 7（4）： 649-657. |
| [5] | 冯霞， 任子云. 基于遗传算法的加油车和摆渡车协同调度研究［J］. 交通运输系统工程与信息， 2016， 16（2）： 155-163. |
| FENG X， REN Z Y. Research on Collaborative scheduling of fuelling vehicle and ferry vehicles based on genetic algorithm ［J］. Journal of Transportation Systems Engineering and Information Technology， 2016， 16（02）： 155-163. （in Chinese）. | |
| [6] | 姜伟华， 张文静， 袁琪， 等. 基于时间窗的机场地面保障车辆动态调度［J］. 科学技术与工程， 2024， 24（3）： 1283-1291. |
| JIANG W H， ZHANG W J， YUAN Q， et al. Dynamic scheduling of airport ground support vehicles based on time window ［J］. Science， Technology and Engineering， 2024， 24（3）： 1283-1291 （in Chinese）. | |
| [7] | WU C L， CAVES R E. Modelling of aircraft rotation in a multiple airport environment［J］. Transportation Research Part E： Logistics and Transportation Review （S1366-5545）， 2002， 38（3-4）： 265-277. |
| [8] | WU C L. Monitoring aircraft turnaround operations-framework development， application and implications for airline operations［J］. Transportation Planning and Technology （S0308-1060）， 2008， 31（2）： 215-228. |
| [9] | MAKHLOOF M A A， WAHEED M E， BADAWI E R， et al. Real-time aircraft turnaround operations manager［J］. Production Planning & Control （S0953-7287）， 2014， 25（1）： 2-25. |
| [10] | 邢志伟， 于瑞文， 李彪， 等. 航班地面保障过程决策支持体系建模［J］. 系统仿真学报， 2024， 36（11）： 2552-2565. |
| XING Z W， YU R W， LI B， et al. Modeling for decision support of flight ground support process ［J］. Journal of System Simulation， 2024， 36 （11）： 2552-2565 （in Chinese）. | |
| [11] | YANG Z， CHEN Y， HU J， et al. Departure delay prediction and analysis based on node sequence data of ground support services for transit flights［J］. Transportation Research Part C： Emerging Technologies， 2023， 153： 104217. |
| [12] | SOCHA V， SPAK M， MATOWICKI M， et al.Predictability of flight arrival times using bidirectional long short-term memory recurrent neural network［J］. Aerospace， 2024， 11（12）： 991. |
| [13] | 李国， 王伟倩， 曹卫东. 基于混合模型的多类型机场航班过站时间预测［J］. 计算机工程与设计， 2025， 46（2）： 633-641. |
| LI G， WANG W Q， CAO W D. Prediction of flights turnaround time for multiple types of airports based on hybrid model［J］. Computer Engineering and Design， 2025， 46（2）： 633-641 （in Chinese）. | |
| [14] | 丁建立， 刘虎， 曹卫东. 航班过站时间预测不确定性量化模型［J/OL］. 北京航空航天大学学报，（2024-12-25）［2025-06-03］. . |
| DING J L， LIU H， CAO W D. Quantitative model of uncertainty for prediction of flight transit time［J/OL］. Journal of Beijing University of Aeronautics and Astronautics， （2024-12-25）［2025-06-03］. （in Chinese）. | |
| [15] | 徐涛， 赵晨旭， 卢敏. 基于因子分析的航班撤轮挡时刻预测方法［J］. 计算机工程与设计， 2017， 38（11）： 3011-3017. |
| XU T， ZHAO C X， LU M. Flight off-block time prediction based on factor analysis［J］. Computer Engineering and Design， 2017， 38 （11）： 3011-3017 （in Chinese）. | |
| [16] | 徐涛， 丁杨， 卢敏. 基于级联BP神经网络的航班撤轮挡时刻预测［J］. 计算机应用与软件， 2019， 36（06）： 226-232. |
| XU T， DING Y， LU M. Flight off-block time prediction based on cascaded bp neural network［J］. Computer Applications and Software， 2019， 36（6）： 226-232 （in Chinese）. | |
| [17] | LIAN G， ZHANG Y， DESAI J， et al. Predicting taxi‐out time at congested airports with optimization-based support vector regression methods［J］. Mathematical Problems in Engineering， 2018， 2018（1）： 7509508. |
| [18] | YIN J， HU Y， MA Y， et al. Machine learning techniques for taxi-out time prediction with a macroscopic network topology［C］∥2018 IEEE/AIAA 37th Digital Avionics Systems Conference （DASC）. Piscataway：IEEE Press， 2018. |
| [19] | LUO Q， CHEN Y， CHEN L， et al. Research on situation awareness of airport operation based on Petri nets［J］. IEEE Access， 2019， 7： 25438-25451. |
| [20] | ZHOU H Y， ZHANG S H， PENG J Q， et al. Informer： beyond efficient transformer for long sequence time-series forecasting［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2021， 35（12）： 11106-11115. |
| [21] | VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［C］∥31st Conference on Neural Information Processing Systems. Sam Diego：NeuraIPS， 2017： 1-11. |
| [22] | ZHOU T， MA Z， WEN Q， et al. FEDformer： Frequency enhanced decomposed transformer for long-term series forecasting［C］∥International Conference on Machine Learning. New York： PMLR， 2022. |
| [23] | WU H， HU T， LIU Y， et al. TimesNet： Temporal 2d-variation modeling for general time series analysis［DB/OL］. arXiv preprint： 2010.02186； 2022. |
| [24] | 梁宏涛， 刘硕， 杜军威， 等. 深度学习应用于时序预测研究综述［J］. 计算机科学与探索， 2023， 17（6）： 1285-1300. |
| LIANG H T， LIU S， DU J W， et al. Review of deep learning applied to time series prediction［J］. Journal of Frontiers of Computer Science and Technology， 2023， 17（6）： 1285-1300 （in Chinese）. | |
| [25] | YU P， PING M， MA J， et al. Method to enhance time series rolling fault prediction by deep fast Fourier convolution［J］. Measurement， 2024， 228： 114177. |
| [26] | JIANG M， ZENG P， WANG K， et al. FECAM： Frequency enhanced channel attention mechanism for time series forecasting［J］. Advanced Engineering Informatics， 2023， 58： 102158. |
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