PURPOSES : The purpose of this study is to understand the effect of installing highway rest areas reflecting continuous driving time as a factor that can affect drowsy driving. METHODS : In this study, various models were tested for representing the effect of reducing accidents of highway rest areas. Among the various models including negative binomial, a series of the zero-inflated models reflecting accidents, the zero-inflated model showed the best suitability. RESULTS : According to the results of the zero inflated negative binomial model, installation of highway rest areas was found to have an accident reduction effect of approximately 11.93%. This is somewhat lower than the accident reduction effect estimated in previous studies. In addition, variables related to continuous driving time were found to affect accidents caused by drowsy driving. CONCLUSIONS : Basically, by using the continuous driving time index, a vehicle that has been driving continuously for more than 2 hours or if the continuous driving time is increased for more than 2 hours, the probability of accidents increases. However, in the case of trucks, a more in-depth study is needed on whether an increase in trucks induces defensive driving of other vehicles and this causes a reduction in accidents. Thus, it is necessary to use the variable cautiously for the highway rest area installation plan.

PURPOSES : The purpose of this study was to develop safety performance functions (SPFs) that use zero-inflated negative binomial regression models for urban intersections in central business districts (CBDs), and to compare the statistical significance of developed models against that of regular negative binomial regression models. METHODS : To develop and analyze the SPFs of intersections in CBDs, data acquisition was conducted for dependent and independent variables in areas of study. We analyzed the SPFs using zero-inflated negative binomial regression model as well as regular negative binomial regression model. We then compared the results by analyzing the statistical significance of the models. RESULTS : SPFs were estimated for all accidents and injury accidents at intersections in CBDs in terms of variables such as AADT, Number of Lanes at Major Roads, Median Barriers, Right Turn with an Exclusive Turn Lane, Turning Guideline, and Front Signal. We also estimated the log-likelihood at convergence and the likelihood ratio of SPFs for comparing the zero-inflated model with the regular model. In he SPFs, estimated log-likelihood at convergence and the likelihood ratio of the zero-inflated model were at -836.736, 0.193 and -836.415, 0.195. Also estimated the log-likelihood at convergence and likelihood ratio of the regular model were at -843.547, 0.187 and -842.631, 0.189, respectively. These figures demonstrate that zero-inflated negative binomial regression models can better explain traffic accidents at intersections in CBDs. CONCLUSIONS : SPFs that use a zero-inflated negative binomial regression model demonstrate better statistical significance compared with those that use a regular negative binomial regression model.