A novel choice model combining utility maximization and the disjunctive decision rules, application to two case studies

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual's choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists’ route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations.