Heterogeneity in choice mechanisms underlying route choice behavior under conditions of uncertainty: A behavioral mixing latent class model

Commonly applied models in travel behavior research to include unobserved heterogeneity are the mixed logit, latent class and a combination of these approaches. In this study, we draw the attention to latent class models since our main interest is the distinct classification of travelers into subgroups. Latent class models identify different segments of individuals who share an identical utility function. However, although these models capture some degree of heterogeneity, they are based on the weak assumption of behavioral homogeneity. By way of contrast, different individuals may apply different behavioral principles to arrive at a choice. To relax this assumption, we use a behavioral mixing model that classifies individuals into latent classes that differ in terms of the decision strategy that individuals apply. Utility maximization and regret minimization are two behavioral choice mechanisms, which may lead to different choices. The present study contributes to the literature on behavioral mixing models by estimating a latent classes, based on different behavioral rules (utility maximizing vs. regret minimization), in which the rules are context-dependent and choices are made under conditions of uncertainty. A further extension pertains to the treatment of the repeated choice nature of the dataset. The data used for this study were collected using a web-based survey, including a stated choice experiment. Route choice is the example used for the decision making process and context variables are incorporated to explore the heterogeneity in travelers' behavior.