Incorporating needs-satisfaction in a discrete choice model of leisure activities

Abstract

In this paper we extend the behavioural scope of discrete choice models for leisure activitytravel choices. More specifically, we investigate to what extent choices for leisure activities and related travels are driven by the satisfaction of needs. In addition to conventional attributes (such as activity costs), our regret based discrete choice model incorporates latent variables representing the anticipated level of individual needs-satisfaction by a particular leisure activity. The latent variables are calibrated with the help of subjective indicators of needs-satisfaction associated with the leisure activities. Results show that needs-satisfaction allows us to decompose a substantial share of the unobserved heterogeneity in leisure activitytravel decisions across respondents. Identifying the structural drivers of anticipated needssatisfaction also enables a better prediction of leisure activity choice.

User satisfaction with train fares: A comparative analysis in five Australian cities

“…In addition, individual characteristics such as age, income, education level, household composition, travel budget, a drivers’ license, and access to a motor vehicle affect mobility behaviour and the level of access to train services [ 9 , 45 – 47 ]. Specifically, in order to attract potential train riders and increase ridership, train services should be designed according to the level of service preferred by current riders [ 13 ].…”

A hybrid choice model with a nonlinear utility function and bounded distribution for latent variables: application to purchase intention decisions of electric cars

“…By employing an ordered choice model as the latent variable model, the HCM can allow for the ordinal nature of the indicator (e.g. Daly et al 2012;Hess, Shires, and Jopson 2013;Bhat and Dubey 2014;Dekker et al 2014;Soto, Cantillo, and Arellana 2014). However, in order to investigate the nonlinear effect of the latent variable based on an ordered model, a more sophisticated approach has to be developed.…”

“…Furthermore, recently, several researchers (e.g. Dekker et al 2014;Hess and Stathopoulos 2014) suggested replacing the utility-maximizing choice model with the random regret minimization model (Chorus, Arentze, and Timmermans 2008) or with a hybrid utility/regret model (Chorus, Rose, and Hensher 2013).…”

“…Walker and Ben-Akiva 2002;Daly et al 2012;Prato, Bekhor, and Pronello 2012;Jensen, Cherchi, and Mabit 2013;Kamargianni, BenAkiva, and Polydoropoulou 2014;Soto, Cantillo, and Arellana 2014), the Bayesian approach (e.g. Bolduc 2013a, 2013b;Dekker et al 2014) and the maximum approximate composite marginal likelihood approach (e.g. Bhat and Dubey 2014).…”

“…Several studies have employed this approach to represent attitudinal indicators in the HCM framework (e.g. Hess, Shires, and Jopson 2013;Bhat and Dubey 2014;Dekker et al 2014;Soto, Cantillo, and Arellana 2014).…”