Strategic Planning Of Civil Airport

Studies on strategic planning of civil airport, mainly includes demonstrating the necessity and feasibility of airport development, analyzing the pre-conditions and influencing factors of airport construction, and exploring the function and location of airport in air transport network, the demand of air transport market, as well as the impact that airport construction and development brought on local economy and state of economy.Firstly this paper introduced the common models for strategic analysis. Combination of various features of the models, this paper established the airport "competition" five forces model. SWOT analysis applied to the analysis development environment of civil airports. This paper further discussed the process and methods of airport development strategies. Take Wuxi airport as an example, the paper analyzed its planning and development environment with SWOT.Secondly, this paper also studied the relevance of the airport development and regional economy in the process of strategic analysis. Used co-integration theory and granger causality test to analyze the interaction between regional economic and air transport. This paper quantitatively analyzed the relationship between regional economic, industrial sector and air transport market in Guangzhou. And using elasticity coefficient method tested adaptation between civil aviation transport and regional economy.Finally, this paper discussed the air traffic demand forecasting models for different airports. For the airports have been built, the paper proposed a combined model in order to forecast the monthly freight volume of civil aviation accurately. This model is composed of the multiple seasonal time series autoregressive integrated moving average model (SARIMA) and grey theory model. Through an empirical analysis, the model of non-linear curve fitting and prediction precision was significantly higher than that of a single model. It can better reflect the dynamic characteristic and the seasonal time series relevancy for monthly freight volume. Thus, it provides a new way for the seasonal time series prediction.