algorithms for deriving and analyzing retroreflectivity influence factors through regression analysis. METHODS : An experimental road lane was created to examine the trends of retroreflectivity and LiDAR intensity values, and a controlled indoor experiment was conducted to identify influencing factors. The optimal algorithm was developed by regression analysis of the experimental data. RESULTS : The significance probability (P-value) through SPSS linear regression analysis was 0.000 for measured height, 0.001 for perpendicular angle, 0.157 for vertical angle, and 0.000 for LiDAR intensity, indicating that measured height, vertical angle, and LiDAR intensity are significant factors because the significance probability is less than 0.05, and vertical angle is not significant. The NNR regression model performed the best, so the measurement data with height (1.2m, 2m, 2.2m) and vertical angle (11.3°, 12.3°, 13.5°) were analyzed to derive the optimal LiDAR Intensity measurement height and vertical angle. CONCLUSIONS : For each LiDAR measurement height and vertical angle, the highest correlation between LiDAR Intensity and retroreflectivity was found at a measurement height of 1.2 meters and a vertical angle of 12.3°, where the model learning accuracy (R2) was the highest.

이 연구는 악천후가 교통 흐름에 영향을 미칠 것이라는 전제하에 악천후 상황 중 강설에 따른 고속도로 용량변화를 분석하기 위한 것으로, 자료수집과 통계분석을 통해 연구를 진행하였다. 분석결과 강설수준에 따른 용량 변화를 살펴보면, 기후 양호시 대비 Light Snow(약한 눈)인 경우 13.2% 감소하였으며, Medium Snow(보통 눈)은 18.6%, Heavy Snow(강한 눈)은 32.0% 감소하는 것으로 나타나 강설수준이 높아질수록 용량감소율은 증가하는 것으로 분석되었다. 기상악화는 도로의 운영 효율을 저하시키는 요인으로 작용할 가능성이 매우 큰 것으로 나타났으며, 이에 따라 향후 이를 고려한 도로 설계 및 운영 방법이 제시되어야 한다.