Analysis of Crash Rates at Freeway Diverge Areas using Bayesian Tobit Modeling Framework

Abstract

The goal of this study is to evaluate the impact of various risk factors on crash rates at freeway diverge areas. Crash rates data for a three-year period from 367 freeway diverge areas were used for analysis. Four candidate Tobit models were developed and compared under the Bayesian framework: a traditional Tobit model; a random parameters Tobit (RP-Tobit) model; a grouped random parameters Tobit (GRP-Tobit) model; and a random intercept Tobit (RI-Tobit). The results showed that the RP-Tobit model performs best with highest value of Rd2 as well as lowest Mean Absolute Deviance (MAD) and Deviance Information Criteria (DIC), indicating the importance of accounting for unobserved heterogeneity to improve the model fit. Both the GRP-Tobit and the RI-Tobit models provide better performance than the traditional Tobit model. The model results showed that crash rates at freeway diverge areas were positively associated with mainline annual average daily traffic (AADT) and negatively associated with ramp AADT, indicating the different mechanisms of the impact of traffic volume on crash rates at freeway diverge areas. Lane-balanced design and high speed limits at freeway diverge areas have a negative effect on crash rates. The number of lanes on mainline and ramp length have significant heterogeneous effects on crash rates across observations. The RP-Tobit model provides a more comprehensive understanding of the heterogeneous effects of risk factors on crash rates across observations.