This project aimed to understand the near-infrared (NIR), intensity, and reflectivity characteristics of LiDAR for measuring retroreflectivity and to understand the correlation between the characteristics of LiDAR and retroreflectivity. A 600 m-testbed was investigated using a survey vehicle equipped with LiDAR, and the testbed retroreflectivity and LiDAR data measurement values were compared. The reflectivity and intensity at night were not affected by sunlight compared with daytime, enabling stable data collection. However, NIR reacted very sensitively to sunlight, and the difference between daytime and nighttime NIR values was very large. In addition, by comparing the absolute error between the retroreflectivity and LiDAR data, we observed that the reflectivity was consistent with the data difference between day and night, and it was not significantly affected by sunlight. However, the intensity showed that the daytime measurement data were more scattered than the nighttime measurement data, resulting in low-precision collection stability caused by sunlight. An analysis of the correlation between retroreflectivity and LiDAR data using 40 data points revealed that the reflectivity and intensity data at night were highly correlated with retroreflectivity, with a P-value of less than 0.05. Reflectivity and intensity values at night correlate with retroreflectivity. The NIR light is sensitive to sunlight. Thus, it can be used as a solar correction index for future retroreflectivity analyses using intensity.

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.