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V. Profillidis (2000)
Econometric and fuzzy models for the forecast of demand in the airport of RhodesJournal of Air Transport Management, 6
B. Roberts (1988)
THE CHANGING FACE OF TRANSPORTATION
(1996)
Analyzing and forecasting freight transportation market in cross-strait direct shipping
(1997)
Applying grey forecasting models to predict international air travel demand for Taiwan area
W.H. Greene (2003)
Econometric Analysis
(2000)
Forecasting the freight of Taichung port import and export cargo
J. Wensveen (1984)
Air transportation, a management perspective
(2003)
Combination carriers and a dedicated air cargo huband - spoke network
G. Liang, T. Han, T. Chou (2005)
Using a Fuzzy Quality Function Deployment Model to Identify Improvement Points in Airport Cargo TerminalsTransportation Research Record, 1935
Anonymous: A Study on Civil Aviation Development in Taiwan Area. Institute of Transportation Ministry of Transportation and Communications
(1998)
Linear regression analysis with fuzzy model
A. Wells (2000)
Airport planning and management
(2000)
The Study of forecast of container traffic by ports in Taiwan area
G.S. Liang, T.C. Han, T.Y Chou (2005)
Using a fuzzy quality function deployment model to identify airport cargo terminal improvement pointsTransp. Res. Rec., 1935
D. Dubois, H. Prade (1978)
Operations on fuzzy numbersInternational Journal of Systems Science, 9
L. Zadeh (1996)
Fuzzy sets
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.
Quality & Quantity – Springer Journals
Published: Aug 26, 2011
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