An EMD-SARIMA-based modeling approach for air traffic forecasting

被引：8
作者：
Nai W. [1 ]
Liu L. [2 ]
Wang S. [1 ]
Dong D. [3 ]
机构：
[1] Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing
[2] Whitman School of Management, Syracuse University, Syracuse, 13244, NY
[3] Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai
来源：
Algorithms | 2017年 / 10卷 / 04期
关键词：
Air traffic forecasting; Empirical mode decomposition; Hybrid modeling; Seasonal autoregressive integrated moving average;
D O I：
10.3390/a10040139
中图分类号：
学科分类号：
摘要：
The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved. © 2017 by the authors.
引用
收藏
相关论文
共 41 条
  • [11] Odoni A.R., The importance of probability theory in the airport and air traffic control sectors, Int. J. Contin. Eng. Educ. Life-Long Learn, 4, pp. 105-113, (1994)
  • [12] Hsu C.I., Wen Y.H., Improved grey prediction models for the trans-Pacific air passenger market, Transp. Plan. Technol, 22, pp. 87-107, (1998)
  • [13] Liu L., Wang H., Qian Z., Wei H., EMD-ARIMA: A hybrid short-term traffic speed forecasting model, Proceedings of the Transportation Research Board 93rd Annual Meeting, (2014)
  • [14] Lippi M., Bertini M., Frasconi P., Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning, IEEE Trans. Intell. Transp. Syst, 14, pp. 871-882, (2013)
  • [15] Chatfield C., The Holt-Winters forecasting procedure, Appl. Stat, 27, pp. 264-279, (1978)
  • [16] Lee S., Fambro D.B., Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting, Transp. Res. Rec, 1678, pp. 179-188, (1999)
  • [17] Van Der Voort M., Dougherty M., Watson S., Combining Kohonen maps with ARIMA time series models to forecast traffic flow, Transp. Res. Part C Emerg. Technol, 4, pp. 307-318, (1996)
  • [18] Kamarianakis Y., Prastacos P., Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches, Trans. Res. Rec, 1857, pp. 74-84, (2003)
  • [19] Kamarianakis Y., Prastacos P., Space-time modeling of traffic flow, Comput. Geosci, 31, pp. 119-133, (2005)
  • [20] Williams B.M., Durvasula P.K., Donald E.B., Urban freeway traffic flow prediction: Application of seasonal autoregressive integrated moving average and exponential smoothing models, Trans. Res. Rec, 1644, pp. 131-144, (1998)
12345