空中交通延误预测研究综述

doi:10.12305/j.issn.1001-506X.2022.03.19

Abstract

Abstract:

The air traffic system is a typical complex system, and its nonlinear aggregate dynamic characteristics challenge the delay prediction, which keeps the problem of air traffic delay prediction open. In the past two decades, a large number of research results have emerged in response to this problem using traditional machine learning, deep learning, complex system modeling and simulation, and queueing theory. Firstly, the reasons for the occurrence and propagation of air traffic delays are explained systematically, and the types of delay predictions in existing research are classified according to the types of prediction values, prediction objects, and prediction time scales. Then, the relative studies at home and abroad are reviewed and the air traffic delays are analyzed. The forecasting methods are classified, the implementation process of representative methods is reviewed in detail, and a comprehensive comparison is made. Finally, based on the existing research problems, the possible future development directions is discussed.

Key words: air traffic, delay prediction, machine learning, modeling and simulation, queueing theory

CLC Number:

V355

Chunzheng WANG, Minghua HU, Lei YANG, Zheng ZHAO. Review on air traffic delay prediction[J]. Systems Engineering and Electronics, 2022, 44(3): 863-874.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Fig.6

Table 2

References 100

1	中国民用航空局. 民航行业发展统计公报[R]. 北京: 中国民航局, 2020: 14.
	Civil Aviation Administration of China. Civil aviation industry development statistics bulletin[R]. Beijing: Civil Aviation Administration of China, 2020: 14.
2	United States Department of Transportation. Understanding the reporting of causes of flight delays and cancellations[EB/OL]. [2020-11-12]. https://www.bts.dot.gov/topics/airlines-and-airports/understanding-reporting-causes-flight-delays-and-cancellations.
3	CHEN Y X , YU J , TSAI S B , et al. An empirical study on the indirect impact of flight delay on China's economy[J]. Sustaina-bility, 2018, 10 (2): 357. doi: 10.3390/su10020357
4	BALL M, BARNHART C, DRESNER M, et al. Total delay impact study: a comprehensive assessment of the costs and impacts of flight delay in the United States[R]. Washington, D C: National Center of Excellence for Aviation Operations Research, 2010: 5-77.
5	MOLINA M, CARRASCO S, MARTIN J. Agent-based mode-ling and simulation for the design of the future european air traffic management system: the experience of cassiopeia[C]//Proc. of the International Conference on Practical Applications of Agents and Multi-Agent Systems, 2014: 22-33.
6	WANG C, ZHANG Z Y, ZHU M. Nonlinear dynamic analysis of air traffic flow at different temporal scales: nonlinear analysis approach versus complex networks approach[C]//Proc. of the IEEE International Conference on Software Quality, Reliability and Security Companion, 2018: 421-431.
7	STERNBERG A, SOARES J, CARVALHO D, et al. A review on flight delay prediction[EB/OL]. [2021-02-28]. http://arxiv.org/abs/1703.06118.
8	刘博, 叶博嘉, 田勇, 等. 航班延误预测方法研究综述[J]. 航空计算技术, 2019, 49 (6): 124- 128. doi: 10.3969/j.issn.1671-654X.2019.06.030
	LIU B , YE B J , TIAN Y , et al. Review of flight delay prediction methods[J]. Aeronautical Computing Technique, 2019, 49 (6): 124- 128. doi: 10.3969/j.issn.1671-654X.2019.06.030
9	罗凤娥, 王波, 李娜, 等. 基于数据挖掘技术的航班延误预测综述[J]. 科技和产业, 2020, 20 (11): 75- 80. doi: 10.3969/j.issn.1671-1807.2020.11.012
	LUO F E , WANG B , LI N , et al. Research review of flight delay prediction based on data mining technology[J]. Science Technology and Industry, 2020, 20 (11): 75- 80. doi: 10.3969/j.issn.1671-1807.2020.11.012
10	WIELAND F. Limits to growth: results from the detailed policy assessment tool[air traffic congestion][C]//Proc. of the AIAA/IEEE 16th Digital Avionics Systems Conference, 1997.
11	COOK A , BELKOURA S , ZANIN M . ATM performance measurement in Europe, the US and China[J]. Chinese Journal of Aeronautics, 2017, 30 (2): 479- 90. doi: 10.1016/j.cja.2017.01.001
12	GULERIA Y , CAI Q , ALAM S , et al. A multi-agent approach for reactionary delay prediction of flights[J]. IEEE Access, 2019, 7, 181565- 181579. doi: 10.1109/ACCESS.2019.2957874
13	HANSEN M, XIONG J, NEXTOR B. Weather normalization for evaluating national airspace system performance[C]//Proc. of the 7th USA/Europe Air Traffic Management Research and Development Seminar, 2007.
14	SCHULTZ M , LORENZ S , SCHMITZ R , et al. Weather impact on airport performance[J]. Aerospace, 2018, 5 (4): 109. doi: 10.3390/aerospace5040109
15	COY S . A global model for estimating the block time of commercial passenger aircraft[J]. Journal of Air Transport Mana-gement, 2006, 12 (6): 300- 305.
16	BORSKY S , UNTERBERGER C . Bad weather and flight delays: the impact of sudden and slow onset weather events[J]. Economics of Transportation, 2019, 18, 10- 26. doi: 10.1016/j.ecotra.2019.02.002
17	XU N , SHERRY L , LASKEY K B . Multifactor model for predicting delays at U.S. airports[J]. Transportation Research Record, 2008, 2052 (1): 62- 71. doi: 10.3141/2052-08
18	LIU Y , HANSEN M . Evaluation of the performance of ground delay programs[J]. Transportation Research Record, 2014, 2400 (1): 54- 64. doi: 10.3141/2400-07
19	DORNDORF U , DREXL A , NIKULIN Y , et al. Flight gate scheduling: state-of-the-art and recent developments[J]. Omega, 2007, 35 (3): 326- 334. doi: 10.1016/j.omega.2005.07.001
20	JARRAH A I , YU G , KRISHNAMURTHY N , et al. A decision support framework for airline flight cancellations and delays[J]. Transportation Science, 1993, 27 (3): 266- 280. doi: 10.1287/trsc.27.3.266
21	KOHL N , LARSEN A , LARSEN J , et al. Airline disruption management-perspectives, experiences and outlook[J]. Journal of Air Transport Management, 2007, 13 (3): 149- 162. doi: 10.1016/j.jairtraman.2007.01.001
22	李俊生, 丁建立. 基于贝叶斯网络的航班延误传播分析[J]. 航空学报, 2008, 29 (6): 1598- 1604. doi: 10.3321/j.issn:1000-6893.2008.06.030
	LI J S , DING J L . Analysis of flight delay propagation using bayesian networks[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29 (6): 1598- 1604. doi: 10.3321/j.issn:1000-6893.2008.06.030
23	CHURCHILL A M , LOVELL D J , BALL M O . Flight delay propagation impact on strategic air traffic flow management[J]. Transportation Research Record, 2010, 2177 (1): 105- 113. doi: 10.3141/2177-13
24	KAFLE N , ZOU B . Modeling flight delay propagation: a new analytical-econometric approach[J]. Transportation Research Part B: Methodological, 2016, 93, 520- 542. doi: 10.1016/j.trb.2016.08.012
25	IVANOV N , NETJASOV F , JOVANOVIĆ R , et al. Air traffic flow management slot allocation to minimize propagated delay and improve airport slot adherence[J]. Transportation Research Part A: Policy and Practice, 2017, 95, 183- 197. doi: 10.1016/j.tra.2016.11.010
26	CAO Y K , ZHU C P , WANG Y J , et al. A method of reducing flight delay by exploring internal mechanism of flight delays[J]. Journal of Advanced Transportation, 2019, 7069380.
27	ABDELGHANY K F , SHAH S S , RAINA S , et al. A model for projecting flight delays during irregular operation conditions[J]. Journal of Air Transport Management, 2004, 10 (6): 385- 394. doi: 10.1016/j.jairtraman.2004.06.008
28	AHMADBEYGI S , COHN A , GUAN Y , et al. Analysis of the potential for delay propagation in passenger airline networks[J]. Journal of Air Transport Management, 2008, 14 (5): 221- 236. doi: 10.1016/j.jairtraman.2008.04.010
29	TUMER K, AGOGINO A. Distributed agent-based air traffic flow management[C]//Proc. of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, 2007.
30	MAYER C , SINAI T . Network effects, congestion externalities, and air traffic delays: or why not all delays are evil[J]. American Economic Review, 2003, 93 (4): 1194- 1215. doi: 10.1257/000282803769206269
31	GAO Q , YAN J , ZHU J F . Airlines'optimization decision of slot allocation in CDM[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11 (5): 94- 98. doi: 10.1016/S1570-6672(10)60144-4
32	VOSSEN T , BALL M . Optimization and mediated bartering models for ground delay programs[J]. Naval Research Logistics, 2006, 53 (1): 75- 90. doi: 10.1002/nav.20123
33	TERRAB M , ODONI A R . Strategic flow management for air traffic control[J]. Operations Research, 1993, 41 (1): 138- 152. doi: 10.1287/opre.41.1.138
34	BALL M O , CHEN C Y , HOFFMAN R , et al. Collaborative decision making in air traffic management: current and future research directions[M]. Berlin: Springer, 2001: 17- 130.
35	YU B , GUO Z , ASIAN S , et al. Flight delay prediction for commercial air transport: a deep learning approach[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 125 (5): 203- 221.
36	MANNA S, BISWAS S, KUNDU R, et al. A statistical approach to predict flight delay using gradient boosted decision tree[C]//Proc. of the IEEE International Conference on Computational Intelligence in Data Science, 2017.
37	WILLMOTT C J , MATSUURA K . Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance[J]. Climate Research, 2005, 30 (1): 79- 82.
38	CHAI T , DRAXLER R R . Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature[J]. Geoscientific Model Development, 2014, 7 (3): 1247- 1250. doi: 10.5194/gmd-7-1247-2014
39	GOPALAKRISHNAN K, BALAKRISHNAN H. A comparative analysis of models for predicting delays in air traffic networks[C]//Proc. of the 12th USA/Europe Air Traffic Management Research and Development Seminar, 2017.
40	ESMAEILZADEH E , MOKHTARIMOUSAVI S . Machine learning approach for flight departure delay prediction and analysis[J]. Transportation Research Record, 2020, 2674 (8): 145- 159. doi: 10.1177/0361198120930014
41	CHOI S, KIM Y J, BRICENO S, et al. Prediction of weather-induced airline delays based on machine learning algorithms[C]// Proc. of the IEEE/AIAA 35th Digital Avionics Systems Confe-rence, 2016.
42	REBOLLO J J , BALAKRISHNAN H . Characterization and prediction of air traffic delays[J]. Transportation Research Part C: Emerging Technologies, 2014, 44, 231- 241. doi: 10.1016/j.trc.2014.04.007
43	BELCASTRO L , MAROZZO F , TALIA D , et al. Using scalable data mining for predicting flight delays[J]. ACM Trans.on Intelligent Systems and Technology, 2016, 8 (1): 1- 20.
44	TU Y , BALL M O , JANK W S . Estimating flight departure delay distributions—a statistical approach with long-term trend and short-term pattern[J]. Journal of the American Statistical Association, 2008, 103 (481): 112- 125. doi: 10.1198/016214507000000257
45	MUELLER E, CHATTERJI G. Analysis of aircraft arrival and departure delay characteristics[C]//Proc. of the AIAA's Aircraft Technology, Integration, and Operations Technical Forum, 2002: 5866.
46	XU N, DONOHUE G, LASKEY K B, et al. Estimation of delay propagation in the national aviation system using Bayesian networks[C]//Proc. of the 6th USA/Europe Air Traffic Management Research and Development Seminar, 2005.
47	XU N, LASKEY K B, CHEN C H, et al. Bayesian network analysis of flight delays[C]//Proc. of the 86th Transportation Research Board Annual Meeting, 2007.
48	DOC 9971. Manual on collaborative air traffic flow management[S]. Montreal: International Civil Aviation Organization, 2018.
49	FERGUSON J , KARA A Q , HOFFMAN K , et al. Estimating domestic US airline cost of delay based on European model[J]. Transportation Research Part C: Emerging Technologies, 2013, 33, 311- 323. doi: 10.1016/j.trc.2011.10.003
50	COOK A J, TANNER G. European airline delay cost reference values[R]. London: University of Westminster, 2011: 1-11.
51	SRIDHAR B , CHEN N Y . Short-term national airspace system delay prediction using weather impacted traffic index[J]. Journal of Guidance, Control, and Dynamics, 2009, 32 (2): 657- 662. doi: 10.2514/1.38798
52	ZOGRAFOS K G , MADAS M A , ANDROUTSOPOULOS K N . Increasing airport capacity utilisation through optimum slot scheduling: review of current developments and identification of future needs[J]. Journal of Scheduling, 2017, 20 (1): 3- 24. doi: 10.1007/s10951-016-0496-7
53	FROLOW I , SINNOTT J H . National airspace system demand and capacity modeling[J]. Proceedings of the IEEE, 1989, 77 (11): 1618- 1624. doi: 10.1109/5.47724
54	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
55	WEISS K R, KHOSHGOFTAAR T M. An investigation of transfer learning and traditional machine learning algorithms[C]// Proc. of the IEEE 28th International Conference on Tools with Artificial Intelligence, 2016: 283-290.
56	CARVALHO L , STERNBERG A , MAIA G L , et al. On the relevance of data science for flight delay research: a systematic review[J]. Transport Reviews, 2020, 14, 21- 30.
57	TAN P N , STEINBACH M , KUMAR V . Introduction to data mining[M]. New Delhi: Pearson Education India, 2016.
58	TORRES-TOLEDANO J G, SUCAR L E. Bayesian networks for reliability analysis of complex systems[C]//Proc. of the Ibero-American Conference on Artificial Intelligence, 1998: 195-206.
59	曹卫东. 基于改进贝叶斯网络结构学习的航班延误波及分析[D]. 天津: 天津大学, 2009.
	CAO W D. Flight delay propagation analysis based on improving Bayesian networks structure learning[D]. Tianjing: Tianjin University, 2009.
60	刘玉洁. 基于贝叶斯网络的航班延误与波及预测[D]. 天津: 天津大学, 2009.
	LIU Y J. The estimation of flight delay and propagation based on Bayesian networks[D]. Tianjing: Tianjin University, 2009.
61	吴薇薇, 孟亭婷, 张皓瑜. 基于机场延误预测的航班计划优化研究[J]. 交通运输系统工程与信息, 2016, 16 (6): 189- 195. doi: 10.3969/j.issn.1009-6744.2016.06.029
	WU W W , MENG T T , ZHANG H Y . Flight plan optimization based on airport delay prediction[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16 (6): 189- 195. doi: 10.3969/j.issn.1009-6744.2016.06.029
62	WU C L , LAW K . Modelling the delay propagation effects of multiple resource connections in an airline network using a Bayesian network model[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 122, 62- 77. doi: 10.1016/j.tre.2018.11.004
63	RODRÍGUEZ S Á , COMENDADOR F G , VALDÉS R A , et al. Assessment of airport arrival congestion and delay: predic- tion and reliability[J]. Transportation Research Part C: Emerging Technologies, 2019, 98, 255- 283. doi: 10.1016/j.trc.2018.11.015
64	陈封能, 斯坦巴赫, 库马尔. 数据挖掘导论[M]. 范明等译. 2版. 北京: 人民邮电出版社, 2011: 168-185.
	CHEN F N, STEINBACH M, KUMAR V. Introduction to data mining[M]. 2nd ed. FAN M, et al. Trans. Beijing: Posts & Telfcom Press, 2011: 168-185.
65	王星. 大数据分析: 方法与应用[M]. 北京: 清华大学出版社, 2013: 63- 67.
	WANG X . Big data analysis: methods and applications[M]. Beijing: Tsinghua University Press, 2013: 63- 67.
66	BREIMAN L . Random forests[J]. Machine Learning, 2001, 45 (1): 5- 32. doi: 10.1023/A:1010933404324
67	HASTIE T , TIBSHIRANI R , FRIEDMAN J . The elements of statistical learning: data mining, inference, and prediction[M]. Berlin: Springer Science & Business Media, 2009.
68	GUI G , LIU F , SUN J L , et al. Flight delay prediction based on aviation big data and machine learning[J]. IEEE Trans.on Vehicular Technology, 2019, 69 (1): 140- 150.
69	郭野晨风, 李杰, 胡明华, 等. 基于简化WITI指标的机场延误预测方法[J]. 交通运输系统工程与信息, 2017, 17 (5): 207- 213.
	GUO Y C F , LI J , HU M H , et al. Airport delay prediction method based on simplified weather impacted traffic index[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17 (5): 207- 213.
70	徐涛, 丁建立, 顾彬, 等. 基于增量式排列支持向量机的机场航班延误预警[J]. 航空学报, 2009, 30 (7): 1256- 1263. doi: 10.3321/j.issn:1000-6893.2009.07.014
	XU T , DING J L , GU B , et al. Forecast warning level of flight delays based on incremental ranking support vector machine[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30 (7): 1256- 1263. doi: 10.3321/j.issn:1000-6893.2009.07.014
71	罗赟骞, 陈志杰, 汤锦辉, 等. 采用支持向量机回归的航班延误预测研究[J]. 交通运输系统工程与信息, 2015, 15 (1): 143- 149, 172. doi: 10.3969/j.issn.1009-6744.2015.01.025
	LUO Y Q , CHEN Z J , TANG J H , et al. Flight delay prediction using support vector machine regression[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15 (1): 143- 149, 172. doi: 10.3969/j.issn.1009-6744.2015.01.025
72	罗谦, 张永辉, 程华, 等. 基于航空信息网络的枢纽机场航班延误预测模型[J]. 系统工程理论与实践, 2014, 34 (S1): 143- 150. doi: 10.12011/1000-6788(2014)s1-143
	LUO Q , ZHANG Y H , CHENG H , et al. Study on flight delay prediction model based on flight networks[J]. System Engineering-Theory & Practice, 2014, 34 (S1): 143- 150. doi: 10.12011/1000-6788(2014)s1-143
73	陈海燕, 王建东, 徐涛. 动态数据驱动的航班延误预测研究[J]. 武汉理工大学学报(交通科学与工程版), 2012, 36 (3): 463- 466. doi: 10.3963/j.issn.2095-3844.2012.03.006
	CHEN H Y , WANG J D , XU T . Research on the dynamic date-driven prediction for flight delay[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2012, 36 (3): 463- 466. doi: 10.3963/j.issn.2095-3844.2012.03.006
74	何洋, 朱金福, 周秦炎. 基于支持向量机回归的机场航班延误预测[J]. 中国民航大学学报, 2018, 36 (1): 30- 36, 41. doi: 10.3969/j.issn.1674-5590.2018.01.007
	HE Y , ZHU J F , ZHOU Q Y . Airport flight delay prediction based on SVM regression[J]. Journal of Civil Aviation University of China, 2018, 36 (1): 30- 36, 41. doi: 10.3969/j.issn.1674-5590.2018.01.007
75	王语桐, 朱金福, 马思思. 基于支持向量回归和线性回归的航班延误组合预测[J]. 武汉理工大学学报(交通科学与工程版), 2019, 43 (3): 426- 431. doi: 10.3963/j.issn.2095-3844.2019.03.010
	WANG Y T , ZHU J F , MA S S . Combination forecast of flight delay based on support vector regression and linear regression[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2019, 43 (3): 426- 431. doi: 10.3963/j.issn.2095-3844.2019.03.010
76	KAMILARIS A , PRENAFETA-BOLDÚ F X . Deep learning in agriculture: a survey[J]. Computers and Electronics in Agriculture, 2018, 147, 70- 90. doi: 10.1016/j.compag.2018.02.016
77	LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 446. doi: 10.1038/nature14539
78	郭丽丽, 丁世飞. 深度学习研究进展[J]. 计算机科学, 2015, 42 (5): 28- 33.
	GUO L L , DING S F . Research progress on deep learning[J]. Computer Science, 2015, 42 (5): 28- 33.
79	KIM Y J, CHOI S, BRICENO S, et al. A deep learning approach to flight delay prediction[C]//Proc. of the IEEE/AIAA 35th Digital Avionics Systems Conference, 2016.
80	KHANMOHAMMADI S , TUTUN S , KUCUK Y . A new multilevel input layer artificial neural network for predicting flight delays at JFK airport[J]. Procedia Computer Science, 2016, 95, 237- 244. doi: 10.1016/j.procs.2016.09.321
81	王慧, 李永亮, 丁辉, 等. 基于深度学习的航班延误预测方法[J]. 指挥信息系统与技术, 2020, 11 (5): 11- 17.
	WANG H , LI Y L , DING H , et al. Flight delay prediction method based on deep learning[J]. Command Information System and Technology, 2020, 11 (5): 11- 17.
82	吴仁彪, 赵娅倩, 屈景怡, 等. 基于CBAM-CondenseNet的航班延误波及预测模型[J]. 电子与信息学报, 2021, 43 (1): 187- 195.
	WU R B , ZHAO Y Q , QU J Y , et al. Flight delay propagation prediction model based on CBAM-condenseNet[J]. Journal of Electronics & Information Technology, 2021, 43 (1): 187- 195.
83	BOSWELL S, EVANS J. Analysis of downstream impacts of air traffic delay[R]. Cambridge: Lincoln Laboratory, Massachusetts Institute of Technology, 1997: 3-45.
84	WONG J T , TSAI S C . A survival model for flight delay propagation[J]. Journal of Air Transport Management, 2012, 23, 5- 11. doi: 10.1016/j.jairtraman.2012.01.016
85	LONG D, LEE D, JOHNSON J, et al. Modeling air traffic management technologies with a queuing network model of the national airspace system[R]. Washington D. C. : National Aeronautics and Space Administration, 1999: 10-125.
86	LONG D, HASAN S. Improved predictions of flight delays using LMINET2 system-wide simulation model[C]//Proc. of the AIAA 9th Aviation Technology, Integration, and Operations Conference and Aircraft Noise and Emissions Reduction Symposium, 2009: 6961.
87	PYRGIOTIS N , MALONE K M , ODONI A . Modelling delay propagation within an airport network[J]. Transportation Research Part C: Emerging Technologies, 2013, 27, 60- 75. doi: 10.1016/j.trc.2011.05.017
88	ODONI A R , ROTH E . An empirical investigation of the transient behavior of stationary queueing systems[J]. Operations Research, 1983, 31 (3): 432- 455. doi: 10.1287/opre.31.3.432
89	HENGSBACH G, ODONI A R. Time dependent estimates of delays and delay costs at major airports[R]. Cambridge: Massachusetts Institute of Technology, 1975: 11-17.
90	MALONE K M. Dynamic queueing systems: behavior and approximations for individual queues and for networks[D]. Cambridge: Massachusetts Institute of Technology, 1995.
91	PETERSON M D , BERTSIMAS D J , ODONI A R . Models and algorithms for transient queueing congestion at airports[J]. Management Science, 1995, 41 (8): 1279- 1295. doi: 10.1287/mnsc.41.8.1279
92	FLEURQUIN P , RAMASCO J J , EGUILUZ V M . Systemic delay propagation in the US airport network[J]. Scientific Reports, 2013, 3 (1): 1- 6.
93	王春政, 胡明华, 杨磊, 等. 基于Agent模型的机场网络延误预测[J]. 航空学报, 2021, 42 (7): 452- 465.
	WANG C Z , HU M H , YANG L , et al. Airport network delay prediction based on Agent model[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (7): 452- 465.
94	MUKHERJEE A , HANSEN M . A dynamic stochastic model for the single airport ground holding problem[J]. Transportation Science, 2007, 41 (4): 444- 456. doi: 10.1287/trsc.1070.0210
95	WANG Y. Prediction of weather impacted airport capacity using ensemble learning[C]//Proc. of the IEEE/AIAA 30th Digital Avionics Systems Conference, 2011: 2D6.
96	BUXI G , HANSEN M . Generating day-of-operation probabilistic capacity scenarios from weather forecasts[J]. Transportation Research Part C: Emerging Technologies, 2013, 33, 153- 166. doi: 10.1016/j.trc.2012.12.006
97	LEIGHFISHER L B . Evaluating airfield capacity[M]. Washington, D C: Transportation Research Board, 2012: 31- 34.
98	BASPINAR B , KOYUNCU E . A data-driven air transportation delay propagation model using epidemic process models[J]. International Journal of Aerospace Engineering, 2016, 4836260.
99	武喜萍, 杨红雨, 韩松臣. 基于复杂网络的空中交通特征与延误传播分析[J]. 航空学报, 2017, 38 (S1): 113- 119.
	WU X P , YANG H Y , HAN S C . Analysis of propreties and delay propagation of air traffic based on complex network[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38 (S1): 113- 119.
100	张兆宁, 张佳. 大面积航班延误发生的预测方法[J]. 系统工程, 2020, 38 (4): 115- 121.
	ZHANG Z N , ZHANG J . Predicition method for the occurrence of large-scale flight delays[J]. Systems Engineering, 2020, 38 (4): 115- 121.

方法类型	模型	聚合/个体延误	延误输出类型	特点
经典机器学习	贝叶斯网络	聚合延误	分类型	适用于小型网络的延误分析; 面对大型复杂网络时, 工作量大; 需要经验知识
	随机森林	聚合延误/个体延误	分类型/连续型	精度较高、无需先验知识, 容易分析变量的重要度
	SVM	个体延误	分类型/连续型	易处理小样本、非线性、高维数
	多元线性回归	聚合延误	连续型	建模简单, 有助于分析各解释变量的重要程度
	BP神经网络	聚合延误	连续型	建模简单
	多元自适应回归样条	聚合延误	连续型	一定程度上能够处理延误变量的非线性关系
深度学习	LSTM-RNN	个体延误	分类型	精度良好
	MLILNN	个体延误	连续型	性能优于传统BP神经网络, 易处理名义变量
	DBN-SVR	个体延误	连续型	易处理高维数据
	CBAM-CondenseNet	个体延误	分类型	能够解决深层网络梯度消散问题, 提升网络预测精度
概率模型	平滑样条-混合分布	个体延误	概率型	延误输出结果能够包含的不确定性理解; 建模的简易性建立在对变量模糊处理的基础上

核心方法类型	工具/方法	网络要素	机场容量设置方法	是否考虑气象状况	过程时间	延误输出类型
建模仿真	NASPAC	机场(不含滑行), 进离场点, 航路扇区, 流控区	包络线方法	否	航空器性能	个体延误
	DPAT	机场(不含滑行)、航路扇区和航路点	包络线方法	否	排列论	个体延误
	Agent-Fleurquin	机场(不含滑行)	计划容量	否	常数/计划时间	个体/聚合延误
	Agent-Date Mining	机场(不含滑行)	数据挖掘	是	数据挖掘	个体/聚合延误
排队论	LMINET	机场(含滑行)、进近管制和航路管制扇区	包络线方法	否	排队论	个体/聚合延误
	LMINET2	机场(含滑行)	公布容量/包络线方法	是	计划减去缓冲时间	个体/聚合延误
	AND	机场(不含滑行)	公布容量	是	计划减去缓冲时间	个体/聚合延误

[1]	Xinmin TANG, Pengcheng ZHENG. IMM aircraft short-term track extrapolation based on geodetic coordinate system [J]. Systems Engineering and Electronics, 2022, 44(7): 2293-2301.
[2]	Xinyu ZHANG, Yuan LIU, Jianing SONG. Short-term orbit prediction based on LSTM neural network [J]. Systems Engineering and Electronics, 2022, 44(3): 939-947.
[3]	Kexin BI, Minggong WU, Wenbin ZHANG, Xiangxi WEN, Kan DU. Modeling and analysis of flight conflict network based onvelocity obstacle method [J]. Systems Engineering and Electronics, 2021, 43(8): 2163-2173.
[4]	Ang LI, Dangmin NIE, Xiangxi WEN, Zekun WANG, Chengxiu YANG. Operation situation assessment of control system based on interdependent network and SVM [J]. Systems Engineering and Electronics, 2021, 43(5): 1287-1294.
[5]	Fan ZHAO, Hu JIN. Communication jamming waveform generation technology based on GAN [J]. Systems Engineering and Electronics, 2021, 43(4): 1080-1088.
[6]	Bo SONG, Wei YE, Xianghui MENG. Review of multi-agent reinforcement learning based dynamic spectrum allocation method [J]. Systems Engineering and Electronics, 2021, 43(11): 3338-3351.
[7]	Fang PENG, Jun WU, Shuai WANG, Jianjun XIANG. Adaptive beamforming algorithm for airborne early warning radar based on SVRGD [J]. Systems Engineering and Electronics, 2021, 43(1): 83-90.
[8]	Yang ZHANG, Guangya SI, Yanzheng WANG. Simulation of unmanned aerial vehicle swarm electromagnetic operation concept [J]. Systems Engineering and Electronics, 2020, 42(7): 1510-1518.
[9]	Weiguo SHI, Ming GUO. Network delay prediction based on model of modified ensemble empirical mode decomposition-permutation entropy and cuckoo search-wavelet neural network [J]. Systems Engineering and Electronics, 2020, 42(1): 184-190.
[10]	ZHANG Yang, SI Guangya, WANG Yanzheng. Modeling of cooperative target allocation of the UAV swarm cyberspace attack action [J]. Systems Engineering and Electronics, 2019, 41(9): 2025-2033.
[11]	SHI Weiguo, XU Chao. Time delay prediction based on phase space reconstruction and robust extreme learning machine [J]. Systems Engineering and Electronics, 2019, 41(2): 416-421.
[12]	ZHANG Jing, HE You, PENG Yingning, LI Gang. Technical features demonstration of the next generation fighter [J]. Systems Engineering and Electronics, 2018, 40(8): 1754-1759.
[13]	WANG Pan, CHEN Yunxiang, CAI Zhongyi, LI Chao. Modeling and repair capability analysis of military aircraft overhaul line [J]. Systems Engineering and Electronics, 2018, 40(6): 1294-1301.
[14]	ZHUANSUN Shaoshuai, YANG Junan, LIU Hui, HUANG Keju. Jamming strategy learning based on positive reinforcement learning and orthogonal decomposition [J]. Systems Engineering and Electronics, 2018, 40(3): 518-525.
[15]	WANG Yi, SUN Jin-biao, XIAO Ming-qing, LUO Ji-xun. Research of dynamic weapon-target assignment problem based on type-2 interval fuzzy K-nearest neighbors classifier [J]. Systems Engineering and Electronics, 2016, 38(6): 1314-1319.

Review on air traffic delay prediction

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 100

Related Articles 15

Recommended Articles

Metrics

Comments