| Citation: | TANG X W，DING Y，ZHANG S R，et al. Taxi-in time prediction of arrival flight[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（7）：2218-2224 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0625
|
Accurate prediction of flight taxi-in time has a significant meaning in allocating aircraft support resources reasonably and improving airport surface movement efficiency. Therefore, a method of taxi-in time prediction based on machine learning model is proposed. It can effectively overcome the deficiency of extensive aircraft arrival time prediction in major airports currently. Using Beijing Capital International Airport as the research object, we firstly analyzed the factors that influence the taxi-in time and created the feature set. Next, we applied various techniques that are commonly used to predict taxi-out times, such as linear regression, K-nearest neighbor, support vector regression, decision tree, random forest, and gradient boosting regression tree, to predict the taxi-in time. The results show that the prediction accuracy of the six machine learning models is over 90% within ±3 min, which means that the construction of the feature set and the selection of models are effective. The gradient boosting regression tree model has the best performance based on the prediction results and model fitting evaluation results. The prediction results of gradient boosting regression tree show that the surface traffic flow features contribute most to the prediction model, and the newly proposed cross-regional feature contributes more than most traditional features.
| [1] |
HEBERT J E, DIETZ D C. Modeling and analysis of an airport departure process[J]. Journal of Aircraft, 1997, 34(1): 43-47. doi: 10.2514/2.2133
|
| [2] |
IDRIS H, CLARKE J P, BHUVA R, et al. Queuing model for taxi-out time estimation[J]. Air Traffic Control Quarterly, 2002, 10(1): 1-22. doi: 10.2514/atcq.10.1.1
|
| [3] |
RAVIZZA S, ATKIN J A D, MAATHUIS M H, et al. A combined statistical approach and ground movement model for improving taxi time estimations at airports[J]. Journal of the Operational Research Society, 2013, 64(9): 1347-1360. doi: 10.1057/jors.2012.123
|
| [4] |
JORDAN R, ISHUTKINA M A, REYNOLDS T G. A statistical learning approach to the modeling of aircraft taxi time[C]// 29th Digital Avionics Systems Conference. Piscataway: IEEE Press, 2010.
|
| [5] |
LEE H, MALIK W, JUNG Y C. Taxi-out time prediction for departures at charlotte airport using machine learning techniques[C]// Proceedings of the 16th AIAA Aviation Technology, Integration, and Operations Conference. Reston, Virginia: AIAA, 2016.
|
| [6] |
HERREMA F, CURRAN R, VISSER H, et al. Taxi-out time prediction model at Charles de Gaulle Airport[J]. Journal of Aerospace Information Systems, 2018, 15(3): 120-130. doi: 10.2514/1.I010502
|
| [7] |
冯霞, 孟金双. 基于KNN和SVR的航班滑出时间预测[J]. 西南交通大学学报, 2017, 52(5): 1008-1014. doi: 10.3969/j.issn.0258-2724.2017.05.023
FENG X, MENG J S. Flight taxi-out time prediction based on KNN and SVR[J]. Journal of Southwest Jiaotong University, 2017, 52(5): 1008-1014(in Chinese). doi: 10.3969/j.issn.0258-2724.2017.05.023
|
| [8] |
DIANA T. Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport[J]. Transportation Research Part E: Logistics and Transportation Review, 2018, 119: 149-164. doi: 10.1016/j.tre.2018.10.003
|
| [9] |
LIAN G, ZHANG Y P, XING Z W, et al. RETRACTED: a new dynamic pushback control method for reducing fuel-burn costs: using predicted taxi-out time[J]. Chinese Journal of Aeronautics, 2019, 32(3): 660-673. doi: 10.1016/j.cja.2018.12.013
|
| [10] |
李楠, 焦庆宇, 张连东, 等. 离场航空器滑行时间预测研究[J]. 重庆交通大学学报(自然科学版), 2021, 40(3): 1-6.
LI N, JIAO Q Y, ZHANG L D, et al. Taxi time prediction of departure aircraft[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(3): 1-6(in Chinese).
|
| [11] |
WANG X W, BROWNLEE A E I, WOODWARD J R, et al. Aircraft taxi time prediction: feature importance and their implications[J]. Transportation Research Part C: Emerging Technologies, 2021, 124: 102892. doi: 10.1016/j.trc.2020.102892
|
| [12] |
RAVIZZA S, CHEN J, ATKIN J A D, et al. Aircraft taxi time prediction: comparisons and insights[J]. Applied Soft Computing, 2014, 14: 397-406. doi: 10.1016/j.asoc.2013.10.004
|
| [13] |
LI N, JIAO Q Y, ZHU X H, et al. Prediction of departure aircraft taxi time based on deep learning[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2020, 37(2): 232-241.
|
| [14] |
陈祯, 唐小卫, 林有超, 等. 基于决策树的航空器离港滑出时间预测方法和模型[J]. 武汉理工大学学报(交通科学与工程版), 2021, 45(3): 448-453.
CHEN Z, TANG X W, LIN Y C, et al. Prediction method and model of aircraft taxi-out time based on decision tree[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2021, 45(3): 448-453(in Chinese).
|
| [15] |
赵征, 冯事成, 宋梅雯, 等. 基于XGBoost的航空器动态滑行时间预测方法研究[J]. 航空工程进展, 2022, 13(1): 76-85.
ZHAO Z, FENG S C, SONG M W, et al. Prediction method of aircraft dynamic taxi time based on XGBoost[J]. Advances in Aeronautical Science and Engineering, 2022, 13(1): 76-85(in Chinese).
|
| [16] |
BROWNLEE A E I, WEISZER M, CHEN J, et al. A fuzzy approach to addressing uncertainty in Airport Ground Movement optimisation[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 150-175. doi: 10.1016/j.trc.2018.04.020
|
| [17] |
沈志远, 胡莹莹. 考虑尾流影响的侧向双跑道机场的跑道容量研究[J]. 南京航空航天大学学报, 2020, 52(1): 161-170.
SHEN Z Y, HU Y Y. Runway capacity of lateral double-runway airport considering wake effect[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2020, 52(1): 161-170(in Chinese).
|
Figures(3) / Tables(8)
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:
