논문 상세보기
pp.127-135
PURPOSES: This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS: Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS: Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.
1. 서론
2. 문헌고찰
3. 자료구축
3.1. 자료수집
3.2. 변수선정
4.1. 노면온도와 도로구간과의 영향관계
4.2. 노면온도와 하늘상태와의 영향관계
4. 영향관계 분석
5. 모형구축 및 분석결과
5.1. 모형구축
5.2. 분석결과
6. 결론
REFERENCES
