• Journal of Korean Society of Transportation
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• v.25 no.2 s.95
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• pp.63-72
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• 2007

The purpose of this study is to develop a traffic characteristic analysis, a correlation analysis with the variables of traffic characteristics, and accident estimation models while recognizing the seriousness of the traffic accidents. The analyses deal with the 181 4-legged signalized intersections that accounted for 1,183 out of 3,115 accidents in Cheongju in 2004. After measuring ADT, intersection area, average lane width, elevation, and other items as independent variables and the number of traffic accidents, the traffic accident rate (accidents per million entering vehicles) and equivalent property damage only (EPDO) figures as dependent variables which are estimated as influencing signalized intersection accidents, the estimation models are developed using correlation analysis and multiple regression analysis. In the analysis of the number of traffic accidents, the model indicates an $R^2$ of 0.612, and five independent variables are taken as significant factors. In the analysis of traffic accident rates, the model indicates an $R^2$ of 0.304 and five significant factors, including intersection area and ADT. Also, for the analysis or the EPDO numbers, which coincides with understanding the seriousness of the traffic accidents and the traffic characteristic analysis, the model indicates an $R^2$ of 0.559, and four independent variables (ADT, main street average lane width, elevation, and speed limit) as significant factors.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.161-170
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• 2008

This study deals with the accident models by vehicle type. The goal is to develop the accident models by vehicle type using the data of 143 4-legged signalized intersections in Cheongju. In pursuing the above, this study gives the particular attentions to explaining the relationships between the values of EPDO(equivalent property damage only) and the traffic and geometric elements. The main results analyzed are the followings. First, 6 negative binomial models are developed, which are all significant at the 90% confidence level. Second, the values of ${\rho}^2$ by vehicle type are 0.14307(auto), 0.35556(large van), 0.21684(small van), 0.205152(motocycle), 0.32338(light-duty truck) and 0.29046(heavy-duty truck), that are all analyzed to be statistically significant. Finally, the common variable included in all models is ADT(average daily traffic), and the specific variable(SV) of auto is analyzed to be the sum of lane width of main road, SV of large van is the average yellow time, and SV of small van is the difference in the number of lane between main and minor road.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.95-101
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• 2011

This study proposes the accident estimation model developed based on the time-series cross-sectional data at 50 intersections in Cheongju. The data were collected repeatedly and accumulated from 2004 to 2007. This study focused on deriving the optimal among the various models including TSCSREG(Time Series Cross Section Regression). Four different models utilizing various elements affecting accidents were developed. Through a statistical test, it was found that the t values of independent variables of the fixed effect models were less than those of the random effect models. Two variables were then found to be positive to the accidents: the number of crosswalks at an intersection and the number of intersections.

• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.141-146
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• 2012

This study deals with the accident model using panel data which are composed of time series data of 2005 through 2007 and cross sectional data of link sections in Cheongju. Panel data are repeatedly collected over time from the same sample. The purpose of the study is to develop the traffic accident model using the above panel data. In pursuing the above, this study gives particular attentions to deriving the optimal models among various models including TSCSREG (Time Series Cross Section Regression). The main results are as follows. First, 8 panel data models which explained the various effects of accidents were developed. Second, $R^2$ values of fixed effect models were analyzed to be higher than those of random effect models. Finally, such the variables as the sum of the number of crosswalk on intersections and sum of the number of intersections were analyzed to be positive to the accidents.