LAUSR

Due to the numerous factors that affect the air passenger traffic in the air transportation market and the randomness of various factors, in addition, the relationship between it and the air passenger traffic is very complicated, so the air passenger traffic forecast in the air transportation market has always been difficult to solve problem. This research mainly discusses the prediction model of air transportation management based on the intelligent algorithm of wireless network communication. This article uses the wireless network communication intelligent algorithm, comprehensively considers the influence of the GDP growth rate, population growth rate, total import and export volume, and other factors on the air transportation market, and draws a relatively complete forecasting model of aviation business volume. In this paper, we use an equal-weight method, linear combination model method, and Bayesian combination model method when selecting the combination forecasting method (these three methods). Because of the parallelism, robustness, nonlinearity, and other characteristics of the Bayesian network method, it adapts to the complex and highly nonlinear characteristics between air passenger traffic and its influencing factors. In the comprehensive prediction of the single model, the different information contained in the single model is used to achieve different combined prediction effects. The economic information and forecasting angle of the system can reduce systematic forecasting errors and optimize the prognostic results, which can make us more intuitively understand the difference of forecasting results brought by different combination forecasting methods. The Theil inequality coefficient of the ARIMA model is 0.004874, and the average absolute percentage error is 0.005914. This research will play a certain guiding role in the development of China’s civil aviation industry.