This paper uses data collected over a five-year period between 2005 and 2009 in Indiana to
estimate random parameters multivariate tobit and zero-inflated count data models of …

In this study, a multivariate random-parameters Tobit model is proposed for the analysis of
crash rates by injury severity. In the model, both correlation across injury severity and …

Relatively recent research has illustrated the potential that tobit regression has in studying
factors that affect vehicle accident rates (accidents per distance traveled) on specific …

In accident injury-severity analysis, an inherent limitation of the traditional ordered probit
approach arises from the a priori consideration of a homogeneous source for the accidents …

This study develops three temporal multivariate random parameters Tobit models to analyze
crash rate by injury severity; these models simultaneously accommodate temporal …

Developing robust and reliable statistical models to estimate, analyze, and understand crash
data is a key element in various highway safety evaluation tasks. Crash data have …

Crashes present different collision types. There usually exist unobserved risk factors which
could jointly affect crash rates of different types, resulting in correlation and heterogeneity …

Random parameters model has been demonstrated to be an effective method to account for
unobserved heterogeneity that commonly exists in highway crash data. However, the …

This study uses highway accident data collected in the State of Washington, between 2011
and 2013, to study the factors that affect accident injury-severities. To account for the fixed …

Abstract In the United States, safety researchers have focused on examining fatal crashes
(involving at least one fatally injured vehicle occupant) by using Fatality Analysis Reporting …