PURPOSES : Although numerous researches have been studied to reveal accident causations for road intersections, there are still many research gaps for road segments. It is mainly because of difficulty of data and lack of analytical method. This study aims to study accident causations for rural road segments and develop accident modification factors for safety evaluation. The accident modification factors can be used to improve road safety. METHODS : Methods for developing AMF are diverse. This study developed AMFs using accident prediction models and selected explanatory variables from the accident models. In order to select final AMFs, three different methods were applied in the study. RESULTS: As a result of the study, many AMFs such as horizontal curves or vertical curves were developed and explained the meanings of the results. CONCLUSIONS : This study introduced meaningful methods for developing significant AMFs and also showed several AMFs. It is expected that traffic or road engineers will be able to use the AMFs to improve road segment safety.

교통의 발달로 많은 수단, 방법의 교통 이동이 생겨나고 있지만 도로는 국내의 화물과 여객의 많은 부 분을 차지하고 있어 그 중요성을 간과할 수 없다. 따라서 도로의 안전성을 판단할 수 있는 기준인 교통사고자료를 통하여 사고에 미치는 영향을 알아보고 그에 대한 해결방안이 필요하다. 본 연구에서는 교통사고에 미치는 영향에 대해서 정량적으로 표현하여 도로의 예방적 차원에서 안전성을 평가할 수 있는 CRF(Crash Reduction Factor)를 산출하는 것을 목적으로 한다. CRF를 개발하기 위해 다양한 접근방법 이 있지만 본 연구는 사고예측모형 개발을 통해 CRF개발하고 선행연구들의 변수 설명력과 비교해보았다. 도로구간의 모형개발의 선행연구들을 보면 노출변수(EXPO)를 상수로 적용(Method 1)하였는데 본 연구에서는 적절한 변수의 계수를 도출하기 위한 다양한 접근방법 중 하나로 노출변수(EXPO)의 적용을 다르게 하여 기존의 방법과 비교하였다. 다른 방법으로 EXPO (AADT×Length×365×10-6)의 노출 변수 중 교통량변수(AADT)를 자연로그에 대입하는 방법(Method 2)이며 또 다른 방법은 EXPO 전체를 자연로그에 대입하는 방법(Method 3)으로 모형을 개발하였다. 위의 방법을 통하여 최종적으로 횡단곡선 및 횡단곡선 반경, 오목구간(종단곡선), 조명시설, 평지, 산 지, 횡단보도에 관한 CRF를 산출하였다. 모형개발에 사용된 사이트 외에 검증을 위하여 30개소 사이트를 이용하여 CRF가 적절한지 MPB, MAD를 통해 검증한 결과 모두 적절하게 분석되었다.

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.