ABSTRACT
Tourism decisions are interdependent of one another. This study aims to explore the interrelations among three pertinent tourism choices, i.e., travel season, transport mode and length of stay, from a data-driven perspective. To achieve a joint model of three travel behavioural decisions, this paper adopts a Frank-type fully nested Archimedean copula function, which is capable of efficiently incorporating a logistic regression model, a multinomial logit model and a lognormal accelerated failure time model. Then, we empirically evaluate the proposed model framework using single-destination Sydney-based trip-level data collected in 2017–2018. Gumbel, Clayton and Frank copula results are compared. 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 transport policy appraisal aimed at tourism recovery or/and development. © 2023 The Author(s)