A study on two-stage selection model of tourism destination at the scale of urban agglomerations

Considering that the demand of tourism destination is variable on the scale of urban agglomeration, the selection process of travel destination is divided into two stages. The traditional transportation combination model based on the multinomial Logit cannot reflect this characteristic. And it is the lack of consideration of the influence of travel distribution and the dynamic transfer of passenger flow between various transport routes. Therefore, this thesis established a combination model of travel demand distribution and transportation assignment with two-stage terminal selection characteristics based on the nested Logit. Based on the analysis of tourists' trip process on the scale of urban agglomeration, a tourist flow transport network with travel destination nest structure is constructed. The generalized cost impedance function of transportation route is constructed based on the direct cost of transportation mode and the indirect cost of travel time. Based on the characteristics of two-stage destination selection of tourists, the form of travel distribution function of tourist flow is given. Through the first-order optimization conditions, it proved that the volume of travel distribution and tourism passenger transport assignment can meet the two-stage equilibrium conditions in the equilibrium state. Based on the idea of MSA algorithm, it designed the solution algorithm of the model and verified the feasibility of the model and algorithm in a simplified example. The calculation results show that the two-stage equilibrium assignment model proposed in this paper can obtain the volume of travel distribution and transportation assignment at the same time, meanwhile compared with the multinomial logit model, the nested Logit structure fully considers the attraction measure of the city destination and the scenic spot destination, which is more in line with the choice behavior of the tourists when choosing the transportation route. Thus, it provides a new comparable method for the optimal allocation of tourism passenger flow transport network resources on the scale of urban agglomeration, and can provide data support for the transportation organization plans of government decision-making departments and tourism transport enterprises.