Accounting for Attribute Non-Attendance and Common-Metric Aggregation in the Choice of Seat Belt Use, a Latent Class Model with Preference Heterogeneity

Abstract

A choice to use a seat belt is largely dependent on the psychology of the vehicles’ occupants, and thus those decisions are expected to be characterized by preference heterogeneity. Despite the importance of seat belt use on the safety of the roadways, the majority of existing studies ignored the heterogeneity in the data and used a very standard statistical or descriptive method to identify the factors of using a seatbelt. Application of the right statistical method is of crucial importance to unlock the underlying factors of the choice being made by vehicles’ occupants. Thus, this study was conducted to identify the contributory factors to the front-seat passengers’ choice of seat belt usage, while accounting for the choice preference heterogeneity. The latent class model has been offered to replace the mixed logit model by replacing a continuous distribution with a discrete one. However, one of the shortcomings of the latent class model is that the homogeneity is assumed across a same class. A further extension is to relax the assumption of homogeneity by allowing some parameters to vary across the same group. The model could still be extended to overlay some attributes by considering attributes non-attendance (ANA), and aggregation of common-metric attributes (ACMA). Thus, this study was conducted to make a comparison across goodness of fit of the discussed models. Beside a comparison based on goodness of fit, the share of individuals in each class was used to see how it changes based on various model specifications. In summary, the results indicated that adding another layer to account for the heterogeneity within the same class of the latent class (LC) model, and accounting for ANA and ACMA would improve the model fit. It has been discussed in the content of the manuscript that accounting for ANA, ACMA and an extra layer of heterogeneity does not just improve the model goodness of fit, but largely impacts the share of class allocation of the models.