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℃ 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.

ABSTRACT
 1. 서론
 2. 문헌고찰
 3. 자료구축
 4. 분석결과
 5. 모형검증
 6. 결론
 REFERENCES

• 김진국(한국건설기술연구원 인프라안전연구본부) | Kim Jin Guk
• 양충헌(한국건설기술연구원 인프라안전연구본부, KICT School 도시 및 교통시스템공학과) | Yang Choong Heon 교신저자
• 문재필(한국건설기술연구원 인프라안전연구본부) | Moon Jae Pil
• 류현주(Jean Monnet University 컴퓨터공학과) | Ryu Hyeon Ju (Jean Monnet University)