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  • (2022) Zhao, Runqing
    Emerging modes of air transport such as autonomous airport shuttle and air taxi are potentially efficient alternatives to current transport practices such as bus and train. This thesis examines bus shuttle service within an airport and air metro as two examples of network design. Within an airport, the bus shuttle serves passengers between the terminals, train stations, parking lots, hotels, and shopping areas. Air metro is a type of pre-planned service in urban air mobility that accommodates passengers for intra- or inter-city trips. The problems are to optimise the service, and the outputs including the optimal fleet size, dispatch pattern and schedule. Based on the proposed time-space networks, the service network design problems are formulated as mixed integer linear programs. The heterogeneous multi-type bus fleet case and stochastic demand case are extended for the airport shuttle case, while a rolling horizon optimisation is adopted for the air metro case. In the autonomous airport inter-terminal bus shuttle case, a Monte Carlo simulation-based approach is proposed to solve the case with demand stochasticity, which is then further embedded into an "effective" passenger demand framework. The "effective" demand is the summation of mean demand value and a safety margin. By comparing the proposed airport shuttle service to the current one, it is found that the proposed service can save approximately 27% of the total system cost. The results for stochastic problem suggest estimating the safety margin to be 0.3675 times of the standard deviation brings the best performance. For the second case, the service network design is extended with a pilot scheduling layer and simulation is undertaken to compare the autonomous (pilot-less) and piloted service design. The results suggest that an autonomous air metro service would be preferable if the price of an autonomous aircraft is less than 1.6 times the price of a human-driven one. The results for rolling horizon optimisation suggest to confirm the actual demand at least 45 minutes prior to departure. Based on data from the Sydney (Australia) region, the thesis provides information directly relevant for the service network design of emerging modes of air transport in the city.

  • (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.