Random Effects Tobit 회귀모형을 이용한 교차로 교통사고 요인 분석
An Analysis on Vehicle Accident Factors of Intersections using Random Effects Tobit Regression Model
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초록
본 연구는 random effects Tobit 회귀모형을 이용하여 도심지 교차로에 대한 교통사고모형을 개발하여 교통사고와 요인간의 상관관계를 파악하는 것이 목적이다. Random effects Tobit 회귀모형의 적용성을 비교ㆍ분석하기 위하여 fixed effect Tobit 회귀모형을 산정하였다. 산정 결과, 교통량, 제한속도, 차로수, 토지이용, 우회전차로, 전방신호등이 유효한 변수로 나타났으며, 총 교통사고율에 대한 random effects 모형의 모형 적합도(결정계수: 0.418, 로그-우도함수값: -3210.103, 우도비: 0.056)와 모형 설명력(MAD: 19.533, MAPE: 75.725, RMSE: 26.886)은 fixed effects 모형의 모형 적합도 (결정계수: 0.298, 로그-우도함수값: -3276.138, 우도비: 0.037) 와 모형 설명력(MAD: 20.725, MAPE: 82.473, RMSE: 27.267)보다 우수한 것으로 나타났으며, 부상교통사고율에 대한 교통사고모형에서도 총 교통사고율의 산정결과와 동일하게 나타나 두 모형에서 random effects Tobit 회귀모형이 다소 우수한 것으로 분석되었다.
Abstract
The study is to develop safety performance functions(SPFs) for urban intersections using random effects Tobit regression model and to analyze correlations between crashes and factors. Also fixed effects Tobit regression model was estimated to compare and analyze model validation with random effects model. As a result, AADT, speed limits, number of lanes, land usage, exclusive right turn lanes and front traffic signal were found to be significant. For comparing statistical significance between random and fixed effects model, random effects Tobit regression model of total crash rate could be better statistical significance with : 0.418, log-likelihood at convergence: -3210.103, :0.056, MAD: 19.533, MAPE: 75.725, RMSE: 26.886 comparing with : 0.298, log-likelihood at convergence: -3276.138, : 0.037, MAD: 20.725, MAPE: 82.473, RMSE: 27.267 for the fixed model. Also random effects Tobit regression model of injury crash rate has similar results of model statistical significant with random effects Tobit regression model.
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