Modeling factors to various drivers' actions due to vehicle malfunctions, the integrated latent class and latent variable model

It is known that how individuals perceive the situation result in choices. However, it unknown, drivers process information of vehicle malfunctions based on at hand. In this study, we only considered drivers' actions cases where vehicles had some forms malfunction time crash. Also due to endogeneity bias, or aggregation error term, did not include directly our model. addition, used latent variable (LV) for class allocation. Although model considering (LCLV) comparable with other models, by means log likelihood, results expanded understanding regarding indirect impact an indicator actions. Due inclusion variables both, and choice relationship between those two models could be revealed impacts variables. was found that, instance, variable, which decreases likelihood driving brake defect, same increases taking driver action speeding. Or female are less likely involved speeding before crashes various malfunctions. Driving under influence drug alcohol being emotional conditions were factors impacting This earliest study implemented technique context traffic safety.