Research On Airline Revenue Influential Factors And Forecast Methods

An important prerequisite for airlines to develop and adjust business strategies is to find out influential factors of airline revenue and forecast it accurately. In this paper an airline revenue forecast model was established basing on factors-choosing and radial basis function neural network (RBFNN) in order to improve forecast accuracy of airline revenue.Firstly, airline revenue influential factors were analyzed from macro and micro aspects, of which, factors of economic development level, fare level, airline capacity and its structural characteristics were focused on, and subsequently load factor, passenger numbers, fare levels, airplane capacity, flight frequency, and GDP were chosen as key influential factors of airline revenue. Then, characteristics of common forecast methods were analyzed and summarized, such as gray prediction method, regression analysis, time series methods and BP neural network forecast method, based on which, RBFNN model was elaborated and used to forecast airline revenue, and the feasibility and the key technologies of RBFNN were studied as well. In the end, Beijing-Chengdu airline's revenue forecast was presented as an example with the chosen influential factors and the forecast aforementioned method of radial basis function neural network. Its results show that the forecast method is highly accurate, and it's applicable to forecast airline revenue, which is of certain directive significance for airlines to design and adjust their business strategies.