• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.135-144
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• 2009

This study estimates the expected number of accidents in Kyungbuk Province to capitalize on experience gained from four years of accident history using the Empirical Bayes (EB) Method. The number of accidents of each site in Kyungbuk Province is recalculated using the Equivalent Property Damage Only (EPDO) method to reflect the severities of the accidents. A cluster analysis is performed to determine similar sites and a unique Safety Performance Function (SPF) is established for each site. The overdispersion parameter is built to correct the difference between the actual number of accidents and the underlying probability distribution. To adjust for varying traffic characteristics of each site, a relative weight is applied and eventually estimates the expected number of accidents. The results show that the highest accident sites are Kimcheon, Youngcheon, and Chilgok, but on the other hand the lowest is Gunwi.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.1 s.12
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• pp.27-37
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• 2007

The automatic speed enforcement system is expected to play an important role as intelligent transport system (ITS) or advanced franc management system (ATMS). It must be a reliable system checking the overspeedy vehicles automatically, while savine the police manpower and ensuring a safe traffic flow. In terms of traffic engineering, the automatic speed enforcement system may serve to improve driver's violent behaviors, facilitate the smooth and safe traffic flow and thereby, reduce the traffic accident. This study was aimed at analyzing the accident before and after installation of the automatic speed enforcement systems at the frequency, EPDO(equivalent property damage only) and accident cost, analyzing the effects of the automatic system on the traffic flow and accident. As a result, when we equip the automatic speed enforcement system on the downward slope section or after middle section comparing with whole section. We should consider the location of automatic speed enforcement system.

• 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.