Comparative study on forecasting method of departure flight baggage demand

In order to provide a scientific basis for the resource allocation in the stage of checked baggage, improve the service efficiency of airport passenger terminal. According to the flight data of an international airport passenger terminal in 2012 May, this paper establish the BP artificial neural network and multiple regression prediction models respectively, in which the influencing factors are decided by grey relationship weight analysis. A set of comparative predictions were done by analyzing three types of data, namely all flight data, single flight data and data of flights with the same destination respectively. The results show that the prediction effect is better when using the last type of data as the sample data and the result of multiple regression model is superior to the BP neural network. It will have great practical significance in the actual source allocation in the stage of checked baggage.