Purchase preferences-based air passenger choice behavior analysis from sales transaction data.

Travel providers such as airlines are becoming more and more interested in understanding how passengers choose among alternative products, especially the purchasing preferences of passengers. Getting information of air passenger choice behavior helps them better display and adapt their offer. Discrete choice models are appealing for airline revenue management (RM). In this paper, we apply latent class multinomial logit model (LC-MNL) to passenger choice behavior. The analysis based on actual sales transaction data reveals the purchase preferences of different passenger types. According to the distribution of the market, we divide passengers into three groups: low-price oriented, high-price oriented and no specific price preference. The low-price oriented passengers only choose products from the set which consists of the lowest price cabin classes of each flight, while the high-price oriented passengers do the opposite. Considering that the passenger types in the transaction sales data are unknown, the latent class passenger choice model can better represent their heterogeneous purchasing preference. An improved EM algorithm is applied to solve the LC-MNL. In the improved EM algorithm, an indicator function containing both the type of passengers and the first choice information in period t is devised, the iterative process of the EM algorithm is more effective consequently. The proposed model and algorithm are evaluated on actual aviation sales transaction data in China. Experimental results show that the passenger choice behavior analysis based on the specific purchasing preferences performs well on actual aviation sales transaction data. • Classify the passengers according to their purchasing preferences and make LC-MNL. • An improved EM algorithm makes the iterative process more effective. • Actual air passenger datasets are applied on verifying our method. [ABSTRACT FROM AUTHOR]