Advertisement

Quality & Quantity

, Volume 45, Issue 6, pp 1539–1550 | Cite as

Application of fuzzy regression on air cargo volume forecast

  • Tsung-Yu Chou
  • Gin-Shuh Liang
  • Tzeu-Chen Han
Research Note

Abstract

This paper presented a Fuzzy Regression Forecasting Model (FRFM) to forecast demand by examining present international air cargo market. Accuracy is one of the most important concerns when dealing with forecasts. However, there is one problem that is often overlooked. That is, an accurate forecast model for one does not necessarily suit the other. This is mainly due to individual’s different perceptions toward their socioeconomic environment as well as their competitiveness when evaluating risk. Therefore people make divergent judgments toward various scenarios. Yet even when faced with the same challenge, distinctive responses are generated due to individual evaluations in their strengths and weaknesses. How to resolve these uncertainties and indefiniteness while accommodating individuality is the main purpose of constructing this FRFM. When forecasting air cargo volumes, uncertainty factors often cause deviation in estimations derived from traditional linear regression analysis. Aiming to enhance forecast accuracy by minimizing deviations, fuzzy regression analysis and linear regression analysis were integrated to reduce the residual resulted from these uncertain factors. The authors applied α-cut and Index of Optimism λ to achieve a more flexible and persuasive future volume forecast.

Keywords

Air cargo Freight forecasting Fuzzy linear regression Index of optimism 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anonymous: A Study on Civil Aviation Development in Taiwan Area. Institute of Transportation Ministry of Transportation and Communications (December, 1996)Google Scholar
  2. Chen, C.Y.: Analyzing and Forecasting Freight Transportation Market in Cross- Strait Direct Shipping. Master thesis, National Cheng Kung University, Taiwan (1998)Google Scholar
  3. Dubois D., Prade H.: Operations on fuzzy number. Int. J. Syst. Sci. 9, 613–626 (1978)CrossRefGoogle Scholar
  4. Greene W.H.: Econometric Analysis. Prentice Hall, Upper Saddle River, NJ (2003)Google Scholar
  5. Hamoen, F.A.M.: Combination Carriers and a Dedicated Air Cargo Hub-and-spoke Network. http://www.tiaca.org./researchpapers/hamoen.html (1999)
  6. Hsu C.I., Wen Y.H.: Applying grey forecasting models to predict international air travel demand for Taiwan area. Transp. Plann. J. 26(3), 525–556 (1997)Google Scholar
  7. Liang G.S., Han T.C., Chou T.Y.: Using a fuzzy quality function deployment model to identify airport cargo terminal improvement points. Transp. Res. Rec. 1935, 130–140 (2005)CrossRefGoogle Scholar
  8. Lin, G.K.: The Study of Forecast of Container Traffic by Ports in Taiwan Area. Master thesis, National Taiwan Ocean University, Taiwan (2000)Google Scholar
  9. Profillidis V.A.: Econometric and fuzzy model for the forecast of demand in the air port of Rhode. J. Air Transp. Manag. 6, 95–100 (2000)CrossRefGoogle Scholar
  10. Su, C.C.: Forecasting the Freight of Taichung Port Import and Export Cargo. Master thesis, National Taiwan Ocean University, Taiwan (1998)Google Scholar
  11. Tanaka H., Vejima S., Asai K.: Linear Regression Analysis with Fuzzy Model. IEEE Trans. Syst. Man Cybern. 12, 903–907 (1982)CrossRefGoogle Scholar
  12. Wells A.T.: Air Transportation: a Management Perspective. Wadsworth publishing Company, Belmont, California (1998)Google Scholar
  13. Wells A.T., Young S.B.: Airport Planning and Management. Mcgraw-Hill, New York, NY (2004)Google Scholar
  14. Zadeh L.A.: Fuzzy sets. Inf. Control 3, 338–353 (1965)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Distribution ManagementNational Chin-Yi University of Technology 35Taiping CityTaiwan, ROC

Personalised recommendations