Incorporating travel time reliability in predicting the likelihood of severe crashes on arterial highways using non-parametric random-effect regression

Travel time reliability (TTR) modeling has gain attention among researchers' due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time. Despite this significant effort, its impact on the severity of a crash is not well explored. This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads. To address the unobserved heterogeneity problem, two random-effect regressions were applied; the Dirichlet random-effect (DRE) and the traditional random-effect (TRE) logistic regression. The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified. The Markov Chain Monte Carlo simulations were adopted to infer the parameters' posterior distributions of the two developed models. Using four-year police-reported crash data and travel speeds from Northeast Florida, the analysis of goodness-of-fit found the DRE model to best fit the data. Hence, it was used in studying the influence of TTR and other variables on crash severity. The DRE model findings suggest that TTR is statistically significant, at 95 percent credible intervals, influencing the severity level of a crash. A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent. Moreover, among the significant variables, alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes. Other significant factors included traffic volume, weekends, speed, work-zone, land use, visibility, seatbelt usage, segment length, undivided/divided highway, and age.