Damp trend Grey Model forecasting method for airline industry

Abstract

This paper presents a modification of the Grey Model (GM) to forecast routes passenger demand growth in the air transportation industry. Forecast methods like Holt-Winters, autoregressive models, exponential smoothing, neural network, fuzzy logic, GM model calculate very high airlines routes pax growth. For this reason, a modification has been done to the GM model to damp trend calculations as time grows. The simulation results show that the modified GM model reduces the model exponential estimations grow. It allows the GM model to forecast reasonable routes passenger demand for long lead-times forecasts. It makes this model an option to calculate airlines routes pax flow when few data points are available.The United States domestic air transport market data are used to compare the performance of the GM model with the proposed model.