| Citation: | DU J H，HU M H，ZHANG W N，et al. Weakly supervised evaluation of airport traffic situation based on metric learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（7）：1772-1778 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0568
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In order to accurately perceive the operating environment of the airport surface, a weakly supervised evaluation method of traffic situation based on metric learning is proposed. Firstly, according to the space-time distribution types of aircraft on the airport surface, the traffic situation index system is constructed from the perspectives of traffic flow, take-off and landing queues, and resource demand; secondly, learning from the metric learning paradigm, the distance measure between situation samples is automatically learned by using pre-defined similar sets and dissimilar sets; on this basis, the K-means algorithm is used to achieve traffic situation classification under weak supervision. Taking the actual operation data of Shanghai Pudong International Airport as an example, the effectiveness of the proposed method is analyzed and verified. The experimental results show that:when the distance coefficients of the departure instantaneous flow at the beginning, the departure cumulative flow, the length of the departure runway queue, and the arrival cumulative flow are greater than 0.5, it has a greater impact on the surface situation; metric learning improves the optimal contour coefficient by 33.3% when compared to the K-means algorithm based on Euclidean distance, and obtains clustering results that meet the e-criterion; in addition, the longer the average taxi time of the airport and the more complex the runway configuration, the higher the level of the traffic situation on the surface.
| [1] |
SIMAIAKIS I, BALAKRISHNAN H. A queuing model of the airport departure process[J]. Transportation Science, 2016, 50(1): 94-109. doi: 10.1287/trsc.2015.0603
|
| [2] |
MARIN A G. Airport management: Taxi planning[J]. Annals of Operations Research, 2006, 143(1): 191-202. doi: 10.1007/s10479-006-7381-2
|
| [3] |
郑丽君, 胡荣, 张军峰, 等. 高峰时段下离港航空器绿色滑行策略设计与评价[J]. 北京航空航天大学学报, 2019, 45(11): 2320-2326. doi: 10.13700/j.bh.1001-5965.2019.0124
ZHENG L J, HU R, ZHANG J F, et al. Design and evaluation of green taxiing strategy for departure aircraft during peak hours[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2320-2326(in Chinese). doi: 10.13700/j.bh.1001-5965.2019.0124
|
| [4] |
RODRÍGUEZ-SANZ Á, COMENDADOR F G, VALDÉS R A, et al. Characterization and prediction of the airport operational saturation[J]. Journal of Air Transport Management, 2018, 69: 147-172. doi: 10.1016/j.jairtraman.2018.03.002
|
| [5] |
SMELTINK J W, SOOMER M J, DE WAAL P R, et al. Optimisation of airport taxi planning: NLRTP-2003-475[R]. Amsterdam : National Aerospace Laboratory NLR, 2003.
|
| [6] |
李善梅, 徐肖豪, 孟令航. 基于聚类神经网络的机场拥挤等级预测[J]. 计算机工程与应用, 2013, 49(17): 254-257. doi: 10.3778/j.issn.1002-8331.1205-0003
LI S M, XU X H, MENG L H. Forecasting of airport congestion level based on cluster and neural network algorithms[J]. Computer Engineering and Applications, 2013, 49(17): 254-257(in Chinese). doi: 10.3778/j.issn.1002-8331.1205-0003
|
| [7] |
谢燕雯. 基于隐马尔可夫过程的机场运行态势预测方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2018: 21-32.
XIE Y W. Research on airport operation situation prediction method based on hidden Markov process[D]. Harbin: Harbin Institute of Technology, 2018: 21-32 (in Chinese).
|
| [8] |
BALAKRISHNA P, GANESAN R, SHERRY L. Airport taxi-out prediction using approximate dynamic programming: Intelligence-based paradigm[J]. Transportation Research Record, 2008, 2052(1): 54-61. doi: 10.3141/2052-07
|
| [9] |
BALAKRISHNA P, GANESAN R, SHERRY L. Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-study of Tampa Bay departures[J]. Transportation Research Part C:Emerging Technologies, 2010, 18(6): 950-962. doi: 10.1016/j.trc.2010.03.003
|
| [10] |
YIN J N, HU M H, MA Y Y, et al. Airport taxi situation awareness with a macroscopic distribution network analysis[J]. Networks and Spatial Economics, 2019, 19(3): 669-695. doi: 10.1007/s11067-018-9402-5
|
| [11] |
CLEWLOW R, SIMAIAKIS I, BALAKRISHNAN H. Impact of arrivals on departure taxi operations at airports[C]//AIAA Guidance, Navigation, and Control Conference. Reston: AIAA, 2010: 7698.
|
| [12] |
XING E P, NG A Y, JORDAN M I, et al. Distance metric learning with application to clustering with side-information[C]//Proceedings of 15th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2002, 521–528.
|
| [13] |
DU J H, HU M H, ZHANG W N. Decision support for aircraft taxi time based on deep metric learning[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems. Piscataway: IEEE Press, 2020: 1-7.
|
| [14] |
LIU Y, SEELHORST M, POZDNUKHOV A, et al. Assessing terminal weather forecast similarity for strategic air traffic management [C]//6th International Conference on Research in Air Transportation. Istanbul: Istanbul Technical University, 2014: 26-30.
|
| [15] |
BOOTH D, FLYNN B, SULTANA J. Airport CDM implementation[EB/OL]. (2021-08-09)[2017-03-31]. https://www.eurocontrol.int/sites/default/files/publication/files/airport-cdm-manual-2017.PDF.
|
| [16] |
ROUSSEEUW P J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational and Applied Mathematics, 1987, 20: 53-65. doi: 10.1016/0377-0427(87)90125-7
|
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