| Citation: | WANG Liwen, LI Biao, XING Zhiwei, 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. doi: 10.13700/j.bh.1001-5965.2020.0165(in Chinese)
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Prediction of ground support process for transit flights is an important function of airport collaborative decision-making system. Aimed at the problems that the refined dynamic prediction of the process cannot be achieved at present and the accuracy is low, a method for dynamic prediction of the transit ground support process based on the Bayesian network is proposed. A Bayesian network model of ground support process was established. The initial sample space generation algorithm based on flight attributes is designed. Dynamic prediction method of ground support process is constructed in conjunction with Gaussian kernel probability density estimation. According to the simulation results of the actual data of a hub airport, it is shown that the method realizes the dynamic prediction of each support node based on full consideration of flight operation attributes. The average absolute error of each node is only 2.224 1 min, and the root mean square error is about 2 min lower than other methods, which confirm that this method can provide an objective decision-making basis for the short-term tactical organization of airport operations.
| [1] |
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): 1-11. doi: 10.2514/1.I010502
|
| [2] |
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. doi: 10.3969/j.issn.1671-1637.2017.02.011
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). doi: 10.3969/j.issn.1671-1637.2017.02.011
|
| [3] |
WEI K J, VIKRANT V, ALEXANDRE J. Airline timetable development and fleet assignment incorporating passenger choice[J]. Transportation Science, 2020, 54(1): 139-163. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=3198882
|
| [4] |
LI M Z, MEGAN S R, HAMSA B. Topological data analysis for aviation applications[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 128(1): 1-11. http://www.sciencedirect.com/science/article/pii/S1366554518314558
|
| [5] |
CADRARSO L, CELISE D R. Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty[J]. Transportation Research, 2017, 81(8): 227-245. http://www.sciencedirect.com/science/article/pii/S0968090X17301560
|
| [6] |
WANG J, GUO H, BAKKER M, et al. An integrated approach for surgery scheduling under uncertainty[J]. Computers & Industrial Engineering, 2018, 118(4): 1-8. http://www.sciencedirect.com/science/article/pii/S0360835218300536
|
| [7] |
LUO Q, CHEN Y R, CHEN L Y, et al. Research on situation awareness of airport operation based on Petri nets[J]. IEEE Access, 2019, 7(1): 25438-25451. http://ieeexplore.ieee.org/document/8649632
|
| [8] |
BARRATT S T, KOCHENDERFER M J, BOYD S P. Learning probabilistic trajectory models of aircraft in terminal airspace from position data[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3536-3545. doi: 10.1109/TITS.2018.2877572
|
| [9] |
CHATI Y S, HAMSA B. Modeling of aircraft takeoff weight using Gaussian processes[J]. Air Traffic Control Quarterly, 2018, 26(2): 70-79. http://smartsearch.nstl.gov.cn/paper_detail.html?id=978ceda4fa7d58e7fe5d6a640c0a8ab4
|
| [10] |
邢志伟, 朱慧, 李彪, 等. 基于贝叶斯网络的航班离港时间动态估计[J]. 计算机科学, 2019, 46(10): 329-335. doi: 10.11896/jsjkx.181102039
XING Z W, ZHU H, LI B, et al. Dynamic estimation of flight departure time based on Bayesian network[J]. Computer Science, 2019, 46(10): 329-335(in Chinese). doi: 10.11896/jsjkx.181102039
|
| [11] |
陈欣, 袁建, 戴靓. 基于空间计量模型的机场网络溢出效应研究[J]. 交通运输系统工程与信息, 2019, 19(4): 211-217. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201904030.htm
CHEN X, YUAN J, DAI L. Airport network spillover effect with spatial econometric models[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 211-217(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201904030.htm
|
| [12] |
沈琳, 于劲松, 唐荻音, 等. 图模型与学习算法结合的贝叶斯网络自动建模[J]. 北京航空航天大学学报, 2016, 42(7): 1486-1493. doi: 10.13700/j.bh.1001-5965.2015.0445
SHEN L, YU J S, TANG D Y, et al. Automatic learning of Bayesian network structure using graph model and learning algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(7): 1486-1493(in Chinese). doi: 10.13700/j.bh.1001-5965.2015.0445
|
| [13] |
KO Y, KIM J, RODRIGUE-ZAS S L. Markov chain Monte Carlo simulation of a Bayesian mixture model for gene network inference[J]. Genes & Genomics, 2019, 41(5): 547-555. doi: 10.1007/s13258-019-00789-8
|
| [14] |
CONTALDI C, VAFAEE F, NELSON P C. Bayesian network hybrid learning using an elite-guided genetic algorithm[J]. Artificial Intelligence Review, 2018, 29(3): 1-28. doi: 10.1007/s10462-018-9615-5
|
| [15] |
邢志伟, 何川, 罗谦, 等. 基于双层k近邻算法航站楼短时客流量预测[J]. 北京航空航天大学学报, 2019, 45(1): 26-34. doi: 10.13700/j.bh.1001-5965.2018.0259
XING Z W, HE C, LUO Q, et al. Terminal building short-term passenger flow forecast based on two-tier k-nearest neighbor algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(1): 26-34(in Chinese). doi: 10.13700/j.bh.1001-5965.2018.0259
|
| [16] |
CAI P L, WANG Y P, LU G Q, et al. A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting[J]. Transportation Research Part C: Emerging Technologies, 2016, 6(2): 21-34. http://www.sciencedirect.com/science/article/pii/S0968090X15003812
|
| [17] |
PROKHORCHUK A, DAUWELS J, JAILLET P. Estimating travel time distributions by Bayesian network inference[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 1867-1876. doi: 10.1109/TITS.2019.2899906
|
| [18] |
CHEN G, GE Z. Hierarchical Bayesian network modeling framework for large-scale process monitoring and decision making[J]. IEEE Transactions on Control Systems Technology, 2020, 28(2): 671-679. doi: 10.1109/TCST.2018.2882562
|
| [19] |
MEGHAN C B. Safety of flight prediction for small unmanned aerial vehicles using dynamic Bayesian networks[D]. Blacksburg: Virginia Polytechnic Institute and State University, 2018.
|
| [20] |
XU B, SHOU Y, LUO J, et al. Neural learning control of strict-feedback systems using disturbance observer[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(5): 1296-1307. doi: 10.1109/TNNLS.2018.2862907
|
| [21] |
谢海红, 戴许昊, 齐远, 等. 短时交通流预测的改进K近邻算法[J]. 交通运输工程学报, 2014, 14(3): 87-94. doi: 10.3969/j.issn.1671-1637.2014.03.017
XIE H H, DAI X H, QI Y, et al. Improved K-nearest neighbor algorithm for short-term traffic forecasting[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 87-94(in Chinese). doi: 10.3969/j.issn.1671-1637.2014.03.017
|
| [22] |
TIAN Z D, LI S J, WANG Y H, et al. A prediction method based on wavelet transform and multiple model fusion for chaotic time series[J]. Chaos, Solitons & Fractals, 2017, 98: 158-172. http://www.sciencedirect.com/science/article/pii/S0960077917300747
|
| [23] |
ROBINSON E, BALLAKRISHNAN H, ABRAMSON M, et al. Optimized stochastic coordinated planning of asynchronous air and space assets[J]. Journal of Aerospace Information Systems, 2017, 14(1): 10-25. doi: 10.2514/1.I010415
|
| [24] |
CARLOS S C, SALAZAR H, MORENO R, et al. Stochastic planning of electricity and gas networks: An asynchronous column generation approach[J]. Applied Energy, 2019, 233(1): 1065-1077. http://www.sciencedirect.com/science/article/pii/S0306261918314594
|
| [25] |
邢志伟, 李彪, 朱慧, 等. 基于深度神经网络的航班保障时间预测研究[J]. 系统仿真学报, 2020, 32(4): 678-686. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202004017.htm
XING Z W, LI B, ZHU H, et al. Research on flight ground service time prediction based on deep neural network[J]. Journal of System Simulation, 2020, 32(4): 678-686(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ202004017.htm
|
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