Improved grey prediction models for the trans-Pacific air passenger market

The rapid economic growth of Asia-Pacific countries continues to result in faster travel growth in the trans-Pacific air passenger market. Grey theory is used to develop time series GM(1,1) models for forecasting total passen ger and 10 country-pair passenger traffic flows in this market. The accumul ated generating operation (AGO) is one of the most important characteristic s of grey theory, and its main purpose is to reduce the randomness of data. The original GM(1,1) models are improved by using residual modifications w ith Markov-chain sign estimations. These models are shown to be more reliab le by posterior checks and to yield more accurate prediction results than A RIMA and multiple regression models. The results indicate that the total nu mber of air passengers in the trans-Pacific market will increase at an aver age annual growth rate of approximately 11% up to the year 2000.