Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

Minho Park, Dongmin Lee

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999-2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

Original languageEnglish
Article number7273630
JournalMathematical Problems in Engineering
Volume2017
DOIs
StatePublished - 2017

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