With a view towards reducing traffic accidents on roadways, various methods have been considered to predict accidents. In this study, we analyze traffic accident frequency models that employ fixed- and random-parameter negative binomial approaches. Random parameters enable the inclusion of unobserved heterogeneity in traffic accident data, which current popular methods with fixed parameters such as Poisson or negative binomial models cannot consider in terms of time variation or segment-specific effects. A continuous, unbalanced panel of accident histories for 208 four-way signalized intersections on national highways in Seoul was used to estimate a traffic accident occurrence model that considered traffic volumes and various geometric characteristics at intersections. The results revealed that the left-turn exclusive lanes and traffic volumes on minor roads had random parameters that affected the likelihood of accident frequencies differently; the other variables were found to significantly affect traffic safety at the intersections on the national highways as fixed parameters. Based on these results, it can be concluded that the same traffic safety facilities have different effects on traffic accidents on major and minor roads. The insights from this study suggest the need for a broader analysis of integrated guidelines for facilities that impact intersection accident propensities.

PURPOSES: The purpose of this study is to develop a traffic accident prediction model using statistical data, to analyze child traffic accidents in school zones. Furthermore, we analyze the factors affecting child traffic accidents, as obtained from the results of the developed model. METHODS : From the literature review, we obtained data for child traffic accidents and various variables relating to road geometry and traffic safety facilities in school zones. We used these variables and data to develop a child traffic accident analysis model. The model was then developed into three types using the Limdep 9.0 statistical tool. RESULTS: As a result of the overdispersion test, the Poisson regression model was applied to all types of models with an overdispersion coefficient of close to zero. The results of the model development are as follows. The main model (all scope of analysis) was for a kindergarten, considering a local roadway, the accessibility of the roadway, the number of unsignalized intersections, and the school zones in commercial area as variables that increase traffic accidents. Sub-model typeⅠ(only the roadway connected to the main entrance) was for a kindergarten, considering a local roadway, skid resistant pavement, no-parking signs, the number of unsignalized intersections, and the number of commercial facilities as variables that increase traffic accidents. The main model and sub-model type Ⅰ showed a reduction in accidents when using forward-type traffic signals. Sub-model typeⅡ(only the roadway not connected to the main entrance) shows that the local roadway is the variable that most increases the probability of traffic accidents. However, when the roadway and walkway are separated, the probability of traffic accidents decreases significantly, by up to 90%. CONCLUSIONS: The results of this study demonstrate the need to restructure the method used to improve school zones. Moreover, the effect of various traffic safety facilities was quantitatively analyzed.

PURPOSES: The purpose of this study is to investigate factors that affect the severity of children’s traffic accidents using the ordered probit model, and to contribute to a safer road environment for children. METHODS: This study used children’s traffic accident data during the last four years in the Incheon Metropolitan area. At this point, to analyze only the direct damage caused to children, the analysis was made of accidents where the victim was under 13 years old. Data from a total of 1,110 accidents was collected. When the model was constructed, as it was judged that there could be a difference in factors affecting accident occurrence depending on the zone characteristics, the model was divided into school and non-school zones. RESULTS: The accident content (severity) is divided into four stages (fatal injury, serious injury, minor injury and injury report) to construct the order-typed probit model. For the analysis, 65 variables of 17 categories were included in the model. The statistical package STATA 13.1 was used to analyze the variables affecting the accident severity with a confidence level of 90% (α·=0.1). Consequently, a total of 15 variables were found to have a statistically significant effect on accident severity in a school zone. In contrast, a total of 22 variables were found to have a statistically significant effect on accident severity in non-school zones. Four variables (daytime, weekday, victim age, intersection) were significant in both models. CONCLUSIONS: Among the significant variables found in school zones, signal violation and type of vehicle (line bus, rent car, bus, business other vehicles) had a relatively greater effect on the accident severity than the other variables. In non-school zones, eight variables comprising daytime, head-on collision, crossing, over-speed, gender of victim (male), victim age, type of vehicle (construction machinery), driver age (50-59) were found to be significant variables. In conclusion, as well as eliminating factors that can lead to accident reductions, it is necessary to consider zone characteristics to reduce the severity of children’s accidents and promote children’s traffic safety.

PURPOSES: This study aims to contribute to a better road environment, which can result in accident reduction from two-wheeled vehicles, by analyzing factors affecting the two-wheeled vehicles’ accident severities in Incheon Metropolitan City. METHODS: In this study, the two-wheeled vehicles’ accident severity was classified into four categories (fatal injury, serious injury, minor injury, and injury report) as a dependent variable, and 97 independent variables out of 14 categories were considered to construct an ordered probit model. To determine the factors affecting accident severity, the statistical package LIMDEP was used. RESULTS: Among the variables used in the analysis, variables related to accident occurrence date (first quarter), region (8-district), accident type (passing the edge of the road of the vehicle for a pedestrian accident, fixed object collision, and overturn of vehicle-only accident), violation type (unobtained safety distance, failure to perform safe driving, violation of intersection driving, and violation of others), the type of road (at the intersection, near the intersection, at the crosswalk, near the crosswalk, etc.), gender of assailant (male), vehicle of victim (pedestrian and motorcycle), and age of victim (under 20) were found to have a statistically significant effect on the severity of the accident. CONCLUSIONS: The variables related to accident type (fixed object collision and overturn of vehicle-only accident), gender of assailant (male), and vehicle of victim (pedestrian and motorcycle) have turned out increasing the accident severity. In addition, accident occurrence for two-wheeled vehicles is more diverse and vulnerable to damage than automobile accidents. Therefore, it is time to recognize the seriousness of two-wheeled vehicle accidents and to improve the environment and systems for safe driving.