Latent Class Model with Heterogeneous Decision Rule for Identification of Factors to the Choice of Drivers’ Seat Belt Use

Mahdi Rezapour,Khaled Ksaibati

doi:10.3390/computation9040044

Abstract

The choice of not buckling a seat belt has resulted in a high number of deaths worldwide. Although extensive studies have been done to identify factors of seat belt use, most of those studies have ignored the presence of heterogeneity across vehicle occupants. Not accounting for heterogeneity might result in a bias in model outputs. One of the main approaches to capture random heterogeneity is the employment of the latent class (LC) model by means of a discrete distribution. In a standard LC model, the heterogeneity across observations is considered while assuming the homogeneous utility maximization for decision rules. However, that notion ignores the heterogeneity in the decision rule across individual drivers. In other words, while some drivers make a choice of buckling up with some characteristics, others might ignore those factors while making a choice. Those differences could be accommodated for by allowing class allocation to vary based on various socio-economic characteristics and by constraining some of those rules at zeroes across some of the classes. Thus, in this study, in addition to accounting for heterogeneity across individual drivers, we accounted for heterogeneity in the decision rule by varying the parameters for class allocation. Our results showed that the assignment of various observations to classes is a function of factors such as vehicle type, roadway classification, and vehicle license registration. Additionally, the results showed that a minor consideration of the heterogeneous decision rule resulted in a minor gain in model fits, as well as changes in significance and magnitude of the parameter estimates. All of this was despite the challenges of fully identifying exact attributes for class allocation due to the inclusion of high number of attributes. The findings of this study have important implications for the use of an LC model to account for not only the taste heterogeneity but also heterogeneity across the decision rule to enhance model fit and to expand our understanding about the unbiased point estimates of parameters.

Highlights

More than 37,000 people died in highway crashes in the U.S in 2017 alone, out of which 47% were not wearing seat belts [1]
The mixed model assumes continuous distributions for random parameters, while the latent class (LC) model relaxes that assumption by using discrete distributions
Decision rule treatment is important when employing an LC model, as otherwise the question of whether the decision makers made their choices based on all the attributes described in the model or some individuals used only some of those criteria would be raised

Summary

Introduction

More than 37,000 people died in highway crashes in the U.S in 2017 alone, out of which 47% were not wearing seat belts [1]. Given the importance of seat belt usage in the enhancement of traffic safety, there has been growing interest in understanding the factors that impact the choice of vehicle occupants in wearing seat belts in order to increase seat belt usage. To achieve that, it is important understand the accurate contributory factors to seat belt usage by implementing the right statistical method that could evaluate unbiased estimated parameters in the choice of seat belt usage. A drivers’ choice for the use of seat belt is expected to be characterized by heterogeneity, which means that data heterogeneity is necessary when making unbiased model outputs estimates. That is especially important because the choice of random parameter distributions has been subjectively made by investigators, and it is usually challenging to come up with a real distribution underlying random parameters

Methods

Results

Conclusion