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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]

Land use and land cover change (LUCC) modeling is crucial to urban planning and policy making. A well-used and effective paradigm for LUCC is the ANN-CA model which employs an artificial neural network (ANN) to calculate a transition probability over each land cell from various driving factors, and then uses a cellular automaton (CA) to evolve all cells consecutively to update their land usage and coverage according to the estimated probability distribution. This paper focuses on the effect of delays or perturbations possibly taking place between land cells on the LUCC modeling. To this end, a new ANN-CA model is proposed which adopts an asynchronous communicating cellular automaton (ACCA), rather than the conventional synchronous CA. Especially, the ACCA allows every cell to communicate with its neighbors independently at random times via a specific protocol, which offers a natural way to include stochastic delays in exchanging the current land usages between cells. As a result, every change of a cell's land use in the ACCA may not affect its neighbors immediately, but is subject to delays that might play an important role in modeling the practical LUCC. Numerical analysis of the new model are carried out over three regions of Chongqing city, China with different scales: Yongchuan District, Sanjiao Town, and Huangguashan Village, and experimental results demonstrate that the proposed ANN-ACCA model can achieve a higher accuracy for LUCC simulation as compared to conventional ANN-CA models. [ABSTRACT FROM AUTHOR]