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  • (2023) Li, Xinming
    As both data and computational access have grown, disaggregate modelling has been gradually growing its popularity in the domain of travel demand modelling and tourism decision modelling. One of the major benefits of disaggregate model is that the actual behaviour of individuals can be observed and captured when a model is developed in a bottom-up way, as opposed to a top-down fashion. In addition, to this behavioural characteristic, disaggregate models are more policy sensitive as the impact of a small change in the market on people’s decisions can be observed and estimated. Tourism decision-making process is commonly perceived as a multifaceted and complex problem. Tourists are usually required to make decisions on a series of behavioural choices including destination, time of travel, transport modes, travel party, Length of stay (LOS) and expenditure, etc. Many tourism researchers demonstrated that these decisions are interrelated in nature. Such interrelations indicate that an intended tourism policy or marketing strategy would have its unintended consequences. Therefore, the objective of this thesis is to explore, introduce and develop model frameworks with highly disaggregate system of models for jointly simulating multiple attributes activities. In the first core chapter (CHAPTER 3), the aim is to develop a hazard-based discrete-continuous model for understanding tourists’ decision making on time of year for travel (seasonal variation) and associated length of stay (LOS) with regard to different travel modes. The model results, which is tested with data from select years between 1999-2018, including years of significant exogenous shocks, show mixed evidence of stability and changes in the parameters. Building on this evidence, this chapter concludes with underlying temporal choice behaviours of tourists that may be of relevance during- and post-COVID19 environment. In the second core chapter (CHAPTER 4), the aim is to extend the previous work to incorporate three tourism decisions by endogenizing transport modes, where a fully nested Archimedean copula structure is adopted for modelling practice. To illustrate its application, the modelling results are used to build a simulated COVID-19 pandemic scenario according to the social distancing restrictions within New South Wales, Australia, and a three-dimensional elasticity analysis for trip destinations is performed. The findings suggest the model provides nuanced insights into simulating tourist behaviours and appraisal of transport policies aimed at tourism recovery or/and development. In the third chapter (CHAPTER 5), the aim is to calibrate a trivariate Archimedean copulas for activity participation, transport modes and accommodation selection while accounting for the correlation between the unobserved heterogeneity of each individual model. Based on the findings of this chapter, we identify the major participants of Australia’s aboriginal activities are senior groups. However, in order to inherit and develop Australia’s indigenous cultural tradition, potential collaborations can be built up among stakeholders such as government, schools/universities, travel agents, bus/coach rental corporates and hotels. Overall, this thesis advances the study and applications of joint models in the domain of multiple tourism modelling. Results of this work provide valuable insights into the interrelations of different tourism decisions, understanding of which can greatly assist in developing more tailored and oriented policies or marketing strategies towards tourists.