| Citation: | TANG X W，DING Y，WU Z L，et al. Dynamic prediction of flight ground service based on cascade[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（5）：1557-1565 （in Chinese）
|
Accurate prediction of flight ground service is the key to achieving fine flight management and improving management efficiency of the airport collaborative decision making (A-CDM) system. Therefore, a multi-node dynamic prediction method for flight ground service based on a cascaded multi-output gradient boosting regression tree model was proposed. The cascaded framework was built to realize the prediction information transmission and result updates between different service schedules. The dynamic prediction algorithm of flight ground service was designed based on gradient boosting regression tree which could be used for multi-node prediction. By taking a typical busy airport as an object, a feature set was constructed, covering flight basic attributes and level information transmission. The results show that the proposed method can effectively realize the dynamic prediction of key node completion time in flight ground service. The initial prediction accuracy of each node within ±5 min reaches more than 80%, and the prediction performance gradually improves as the flight ground service continues. The final prediction accuracy of over 60% of nodes within ±5min exceeds 95%. It provides effective method support for improving the flight operation predictability and the collaborative decision making ability of multi-agents in airports.
| [1] |
MILLER T, LU B, STERLING L, et al. Requirements elicitation and specification using the agent paradigm: the case study of an aircraft turnaround simulator[J]. IEEE Transactions on Software Engineering, 2014, 40(10): 1007-1024.
|
| [2] |
GARCÍA ANSOLA P, GARCÍA HIGUERA A, OTAMENDI F J, et al. Agent-based distributed control for improving complex resource scheduling: application to airport ground handling operations[J]. IEEE Systems Journal, 2014, 8(4): 1145-1157.
|
| [3] |
冯霞, 张鑫, 陈锋. 飞机过站上客过程持续时间分布[J]. 交通运输工程学报, 2017, 17(2): 98-105.
FENG X, ZHANG X, CHEN F. Boarding duration distribution of aircraft turnaround[J]. Journal of Traffic and Transportation Engineering, 2017, 17(2): 98-105(in Chinese).
|
| [4] |
SCHULTZ M, EVLER J, ASADI E, et al. Future aircraft turnaround operations considering post-pandemic requirements[J]. Journal of Air Transport Management, 2020, 89: 101886.
|
| [5] |
POHLING O, SCHIER-MORGENTHAL S, LORENZ S. Looking into the crystal ball: how automated fast-time simulation can support probabilistic airport management decisions[J]. Aerospace, 2022, 9(7): 389.
|
| [6] |
EVLER J, ASADI E, PREIS H, et al. Airline ground operations: schedule recovery optimization approach with constrained resources[J]. Transportation Research Part C: Emerging Technologies, 2021, 128: 103129.
|
| [7] |
SHEIBANI K. Scheduling aircraft ground handling operations under uncertainty using critical path analysis and Monte Carlo simulation[J]. International Journal of Business Strategy and Automation, 2020, 1(1): 37-45.
|
| [8] |
邢志伟, 吴兵, 罗晓, 等. 基于AFSA-RVM的航班保障服务时间状态估计[J]. 计算机仿真, 2020, 37(8): 35-39.
XING Z W, WU B, LUO X, et al. State estimation about service time of flight support based on AFSA-RVM[J]. Computer Simulation, 2020, 37(8): 35-39(in Chinese).
|
| [9] |
邢志伟, 韩大浩, 罗谦. 基于改进GA的神经网络航班保障时间估计[J]. 计算机工程与设计, 2020, 41(1): 107-114.
XING Z W, HAN D H, LUO Q. Estimation of flight support time based on improved GA neural network[J]. Computer Engineering and Design, 2020, 41(1): 107-114(in Chinese).
|
| [10] |
王立文, 李彪, 邢志伟, 等. 过站航班地面保障过程动态预测[J]. 北京航空航天大学学报, 2021, 47(6): 1095-1104.
WANG L W, LI B, XING Z W, et al. Dynamic prediction of ground support process for transit flight[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1095-1104(in Chinese).
|
| [11] |
MA Y L, JAMES HOUSDEN R, FAZILI A, et al. Real-time registration of 3D echo to X-ray fluoroscopy based on cascading classifiers and image registration[J]. Physics in Medicine and Biology, 2021, 66(5): 055019.
|
| [12] |
TIAN M Z, LIU L, LU J Y, et al. Vehicle recognition based on haar features and adaboost cascade classifier[J]. Journal of Physics: Conference Series, 2022, 2303(1): 012052.
|
| [13] |
王宁, 曹萃文. 基于XGBoost模型的炼油厂氢气网络动态多输出预测模型[J]. 华东理工大学学报(自然科学版), 2020, 46(1): 77-83.
WANG N, CAO C W. A dynamic multi-output prediction model of the hydrogen network in a real-world refinery based on XGBoost model[J]. Journal of East China University of Science and Technology, 2020, 46(1): 77-83(in Chinese).
|
| [14] |
ALHAKEEM Z M, JEBUR Y M, IMRAN H, et al. Prediction of ecofriendly concrete compressive strength using gradient boosting regression tree combined with gridsearchCV hyperparameter-optimization techniques[J]. Materials, 2022, 15(21): 7432.
|
| [15] |
HUANG Y F, LIU Y H, LI C H, et al. GBRTVis: online analysis of gradient boosting regression tree[J]. Journal of Visualization, 2019, 22(1): 125-140.
|
| [16] |
WANG T, HU S H, JIANG Y. Predicting shared-car use and examining nonlinear effects using gradient boosting regression trees[J]. International Journal of Sustainable Transportation, 2021, 15(12): 893-907.
|
| [17] |
中国民用航空局. 机场协同决策系统技术规范: MH/T 6125—2022[S]. 北京: 中国民用航空局, 2022.
Civil Aviation Administration of China. Technical specifications for airport collaborative decision-making system: MH/T 6125—2022[S]. Beijing: Civil Aviation Administration of China, 2022(in Chinese).
|
Figures(7) / Tables(6)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
