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

Abstract

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

Hybrid random utility-random regret model in the presence of preference heterogeneity, modeling drivers’ actions

“…In another study, only drivers actions in case where vehicles has some forms of malfunctions at the time of crash was considered (Rezapour & Ksaibati, 2022). In the model, due to the endogeneity bias, vehicle malfunctions were indirectly modeled by means of latent variable (LV).…”