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자료유형
학술저널
저자정보
김진국 (한국건설기술연구원 인프라안전연구본부) 양충헌 (한국건설기술연구원 인프라안전연구본부) 류현주 (Jean Monnet University 컴퓨터공학과) 문재필 (한국건설기술연구원 인프라안전연구본부)
저널정보
한국도로학회 한국도로학회논문집 한국도로학회논문집 제20권 제5호
발행연도
2018.1
수록면
121 - 128 (8page)

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PURPOSES : This study aimed to evaluate the performance of a model developed for road surface temperature change pattern in reflecting specific road characteristics. Three types of road sections were considered, namely, basic, tunnel, and soundproof tunnel. METHODS : A thermal mapping system was employed to collect actual road surface temperature and locational data of the survey vehicle. Data collection was conducted 12 times from 05:30 am to 06:30 am on the test route, which is an uninterrupted flow facility. A total of 9010 road surface temperature data were collected, and half of these were selected based on a random selection process. The other half was used to evaluate the performance of the model. The model used herein is based on machine learning algorithms. The mean absolute error (MAE) was used to evaluate the accuracy of the estimation performance of the model. RESULTS : The MAE was calculated to determine the difference between the estimated and the actual road surface temperature. A MAE of $0.48^{\circ}C$ was generated for the overall test route. The basic section obtained the smallest error whereas that of the tunnel was relatively high. CONCLUSIONS:The road surface temperature change is closely related to the air temperature. The process of data pre-processing is very important to improve the estimation accuracy of the model. Lastly, it was difficult to determine the influence of the data collection date on the estimation of the road surface temperature change pattern due to the same weather conditions.

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