中国民航大学学报 ›› 2023, Vol. 41 ›› Issue (3): 1-7.
• 民用航空 • 下一篇
陈斌1，2，吴瑾1
修回日期:2023-01-12
接受日期:2023-03-08
出版日期:2023-06-27
发布日期:2023-10-27
作者简介:陈斌（1985—），男，山西临汾人，正高级工程师，博士，研究方向为航空运输市场经济
基金资助:CHEN Bin1，2 , WU Jin1
Revised:2023-01-12
Accepted:2023-03-08
Online:2023-06-27
Published:2023-10-27
中图分类号:
陈斌, 吴瑾. 机场旅客吞吐量预测方法研究综述[J]. 中国民航大学学报, 2023, 41(3): 1-7.
CHEN Bin , WU Jin. Review of prediction methods of airport passenger throughput[J]. Journal of Civil Aviation University of China, 2023, 41(3): 1-7.
| [1] | TSEKERIS T, TSEKERIS C. Demand forecasting in transport: over view and modeling advances[J]. Economic Research-Ekonomska Istra觩ivanja, 2011, 24(1): 82-94. |
| [2] | NICOLAISEN M S, DRISCOLL P A. Ex-post evaluations of demand forecast accuracy: a literature review[J]. Transport Reviews, 2014, 34(4): 540-557. |
| [3] | LI C, XIAO M T. A review of research on airline passenger volume fore－ casting [C]//Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer(MACMC 2017), November 27-29, 2017, Xi'an, China． Paris, France: Atlantis Press, 2018. |
| [4] | WANG X Q, HYNDMAN R J, LI F, et al Forecast combinations: an over 50-year review[EB/OL]. (2022-09-23)[2023-01-05]. https://arxiv.org/ abs/2205.04216. |
| [5] | BOX G E P, JENKINS G M, REINSEL G C, et al Time series analysis: forecasting and control[M]. 5th ed. New York: John Wiley & Sons Inc., 2015. |
| [6] | LIU S Y, LIU S, TIAN Y, et al Research on forecast of rail traffic flow based on ARIMA model[J]. Journal of Physics Conference Series, 2021, 1792(1): 012065. |
| [7] | WILLIAMS B M, DURVASULA P K, BROWN D E, et al Urban free－ way traffic flow prediction: application of seasonal autoregressive inte－ grated moving average and exponential smoothing models[J]. Transportation Research Record: Journal of the Transportation Research Board, 1998, 1644(1): 132-141. |
| [8] | KAN T W H, HATICE O B, ANDREW G, et al Forecasting of Hong Kong Airport's passenger throughput[J]. Tourism Management, 2013, 42: 62-76. |
| [9] | CHATFIELD C. The Holt-Wintersforecasting procedure[J]. Applied Statistics, 1978(27): 264-279. |
| [10] | BERMU＇ DEZ J D, SEGURA J V, VERCHER E. Holt-Winters forecast－ ing: an alternative formulation applied to UK air passenger data[J]. Jour－ nal of Applied Statistics, 2007, 34(9): 1075-1090. |
| [11] | 孟照宇.基于灰色系统模型的辽宁省需水量预测研究[J].黑龙江水6--利科技,2020,48(4):33-36. |
| [12] 邱 慧, 黄解宇, 董亚兰. 基于灰色系统模型的山西省物流需求预 测分析[J]. 数学的实践与认识, 2016, 46(13): 66-70. | |
| [13] WONG H L. Time series forecasting with stochastic Markov models based on fuzzy set and grey theory[J]. Applied Mechanics and Materi－ als, 2015, 764/765: 975-978. | |
| [14] DONG W H. Empirical analysis on the relationship between civil avia－ tion transportation industry and GDP in China based on VAR model[J]. | |
| Journal of Finance and Economics, 2009, 35(8): 69-78. | |
| [15] WU X L, MAN S. Air transportation in China: temporal and spatial evo－ lution and development forecasts[J]. Journal of Geographical Sciences, 2018, 28(10): 1485-1499. | |
| [16] ZHANG F N, GRAHAM D J. Air transport and economic growth: a re－ viewofthe impact mechanism and causal relationships[J]. Transport Re－ views, 2020, 40(4): 506-528. | |
| [17] BOONEKAMP T, ZUIDBERG J, BURGHOUWT G. Determinants of air travel demand: the role of low-cost carriers, ethnic links and aviationdependent employment[J]. Transportation Research Part A: Policy and Practice, 2018, 112: 18-28. | |
| [18] KIM S, SHIN D H. Forecasting short-term air passenger demand using big data from search engine queries[J]. Automation in Construction, 2016, 70: 98-108. | |
| [19] SIMS C A. Macroeconomics and reality[J]. Econometrica, 1980, 48(1): 1-48. | |
| [20] SMITH B L, DEMETSKY M J. Short-term traffic flow prediction mod－ els-a comparision of neural network and nonparametric regression ap－ proaches[C]//1994 IEEE International Conference on Systems, Man, and Cybernetics-Humans, Information and Technology (SMC 94), Oc－ tober 2-5, 1994, San Antonio, Tx, USA. IEEE, 1994: 1706-1709. | |
| [21] GUNTER U, ZEKAN B. Forecasting air passenger numbers with a GVAR model [J]. Annals of Tourism Research, 2021, 89: 103252. | |
| [22] BATES J M, GRANGER C W J. The combination of forecasts[J]. Journal of the Operational Research Society, 1969, 20(4): 451-468. | |
| [23] BLANC S M, SETZER T. Bias-variance trade-off and shrinkage of weights in forecast combination[J]. Management Science, 2020, 66(12): 5720-5737. | |
| [24] GASTINGER J, NICOLAS S, STEPIC＇ D, et al A study on ensemble learning for time series forecasting and the need for meta-learning[C]// 2021 International Joint Conference on Neural Networks(IJCNN), July 18-22, 2021, Shenzhen, China. IEEE, 2021. | |
| [25] ZHANG Y, ZHANG J. A hybrid model of neural network and grey theo－ ry for air traffic passenger volume forecasting[J]． Key Engineering Mate－ rials, 2010, 439/440: 818-822． [26] SUN S L, LU H X, TSUI K L, et al Nonlinear vector auto-regression neural network for forecasting air passenger flow[J]． Journal of Air Transport Management, 2019, 78: 54-62. | |
| [27] XU S J, CHAN H K, ZHANG T T. Forecasting the demand of the avia－ tion industry using hybrid time series SARIMA-SVR approach[J]. Trans－ portation Research Part E: Logistics and Transportation Review, 2019, 122: 169-180. | |
| [28] LI Y X, JIANG X S. Airport passenger throughput forecast based on PSO-SVR model[J]. IOP Conference Series: Materials Science and En－ gineering, 2020, 780(6): 062006. | |
| [29] ZHOU W J, CHEN Y K, DING S, et al A grey seasonal least square support vector regression model for time series forecasting[J]. ISA Transactions, 2021, 114: 82-98. | |
| [30] NAI W, LIU L, WANG S Y, et al An EMD-SARIMA-based modeling approach for air traffic forecasting[J]. Algorithms, 2017, 10(4): 139. | |
| [31] BAO Y K, XIONG T, HU Z Y. Forecasting air passenger traffic by sup－ port vector machines with ensemble empirical mode decomposition and slope-based method[J]. Discrete Dynamics in Nature and Society, 2012, 2012: 1-12. | |
| [32] DANTAS T M, CYRINO OLIVEIRA F L, VARELA REPOLHO H M. | |
| Air transportation demand forecast through Bagging Holt Winters meth－ ods [J]. Journal of Air Transport Management, 2017, 59: 116-123. | |
| [33] JIN F, LI Y W, SUN S L, et al Forecasting air passenger demand with a new hybrid ensemble approach[J]. Journal of Air Transport Management, 2020, 83: 101744. | |
| [34] LIANG X Z, ZHANG Q, HONG C X, et al Do internet search data help forecast air passenger demand? evidence from China’s airports[J]. Frontiers in Psychology, 2022, 13: 809954. | |
| [35] XIAO Y, LIU J J, HU Y, et al A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting [J]. | |
| Journal of Air Transport Management, 2014, 39: 1-11. | |
| [36] GANG X, WANG S Y, LAI K K. Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches[J]. Journal of Air Transport Management, 2014, 37: 20-26. | |
| [37] ALTAY N, GREEN III W G. OR/MS research in disaster operations management[J]. European Journal of Operational Research, 2006, 175 (1): 475-493. | |
| [38] PRIYADARSHANA M. Modeling air passenger demand in Bandaranaike International Airport, Sri Lanka[J]. Journal of Business & Economic Pol－ icy, 2015, 2(4): 146-151. | |
| [39] LI X S, DE GROOT M, B魨CK T. Using forecasting to evaluate the im－ pact of COVID-19 on passenger air transport demand[J]. Decision Sci－ ences, 2021, 52(4): 1-16. | |
| [40] KITSOU S P, KOUTSOUKIS N S, CHOUNTALAS P, et al International passenger traffic at the Hellenic Airports: impact of the COVID-19 pan－ demic and mid-term forecasting[J]. Aerospace, 2022, 9(3): 143. | |
| [41] BARCZAK A, DEMBIN＇ SKA I, ROZMUS D, et al The impact of COVID19 pandemic on air transport passenger markets -implications for se－ lected EU airports based on time series models analysis[J]. Sustainabili－ ty, 2022, 14(7): 4345. | |
| [42] MENG F Y, GONG W W, LIANG J, et al Impact of different control policies for COVID-19 outbreak on the air transportation industry: a comparison between China, the US and Singapore[J]. PLoS One, 2021, 16(3): 0248361. | |
| [43] 罗建锋, 周凌云, 李 伟. 基于 BP 神经网络的新建支线机场货邮量 综合预测[J]. 江苏商论, 2012(2): 47-49. | |
| [44] 王 喆, 夏洪山. 基于熵权法的新机场货邮吞吐量类比分析预测[J]. | |
| 郑州大学学报(工学版), 2008, 29(4): 141-144. | |
| [45] 中国民用航空局. 2021 年民航行业发展统计公报[R]. 北京: 中国民 用航空局, 2022. |
| [1] | 赵向领, 左蕾, 李云飞, 徐吉辉. 民用航空运输载重配平问题研究综述[J]. 中国民航大学学报, 2024, 42(1): 1-9. |
| [2] | 丁新伟, 秦倩, 阚犇, 刘骐畅, 贾驰. 基于图卷积神经网络的航站楼旅客流时空分布预测[J]. 中国民航大学学报, 2023, 41(6): 31-36. |
| [3] | 杨磊, 雷梦尧. 基于时频分析的雷达非匀速运动目标参数估计[J]. 中国民航大学学报, 2023, 41(4): 29-36. |
| [4] | 靳慧斌, 冯朝辉, 张召悦, 王志森. 基于改进KNN检测的ADS-B轨迹插补研究[J]. 中国民航大学学报, 2023, 41(4): 23-28. |
| [5] | 吴越, 亚军, 孙现玲, 周景贤, 吕宗平. 基于区块链的民航网络安全威胁情报共享方案[J]. 中国民航大学学报, 2023, 41(4): 16-22. |
| [6] | 史永胜, 杜坤, 李莎. 基于响应面模型的安全箱卡扣强度优化[J]. 中国民航大学学报, 2023, 41(3): 59-64. |
| [7] | 高建树, 王庆强. 水雾喷射角对半封闭机舱火灾灭火效果模拟研究[J]. 中国民航大学学报, 2023, 41(3): 53-58. |
| [8] | 王亚锋, 刘继军. 航空座椅动态冲击试验最严酷工况选择研究[J]. 中国民航大学学报, 2023, 41(3): 47-52. |
| [9] | 王永刚, 车卓君. 飞行员不安全行为的内在影响因素研究[J]. 中国民航大学学报, 2023, 41(3): 41-46. |
| [10] | 黄妍妮 , 陈 丰 , Pak-wai Chan. 地形对香港国际机场起降航道低空风切变的影响[J]. 中国民航大学学报, 2023, 41(3): 31-40. |
| [11] | 岳仁田, 张知波. 基于云模型的空管运行系统脆弱性评估[J]. 中国民航大学学报, 2023, 41(3): 25-30. |
| [12] | 吕自鹏, 张建军, 吕琳, 韩明, 邓琪. 一种北斗GEO卫星弱信号捕获方法[J]. 中国民航大学学报, 2023, 41(3): 19-24. |
| [13] | 谢春生, 张旭. 基于改进人工势场法的危险天气下路径改航[J]. 中国民航大学学报, 2023, 41(3): 13-18. |
| [14] | 陈志杰, 朱永文, 刘杨. 基于数字化空域系统的城市无人机管理对策研究[J]. 中国民航大学学报, 2023, 41(3): 8-12. |
| [15] | 王鹏, 姚鑫鹏, 汪克念, 陈文琪, 陈曦. 基于大感知域LSTM-Seq2Seq模型的代码缺陷检测方法[J]. 中国民航大学学报, 2023, 41(2): 14-20. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||
