Passenger traffic forecast based on the Grey-Markov method

Passenger traffic forecast is significant for the study of the change of passenger transport capacity. Forecasting passenger traffic scientifically is very important for decision-making of transportation development strategies. This paper analyzes the factors of passenger traffic, and describes the principle of the grey-Markov chain. Then we divide these factors into two categories: supply factors and affecting factors. Based on the characters of passenger traffic and the advantages of grey model and Markov chain, this paper gives grey-Markov chain model for forecasting passenger traffic. Grey model forecasting curve shows the developing of passenger traffic. Markov model reflects the volatility law and then optimizes the result. Through the empirical research of the data of passenger turnover from 1990 to 2007, the following conclusions are drawn: the Markov Chain method may help to modify the forecast accuracy to a great extent; Passenger traffic development is known from the GM (1, 1) forecast values and the maximum Markov state transition probability. Forecasting the fluctuations and random passenger traffic by the Grey-Markov model can have a higher rate of accuracy.