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Developing Models for Patterns of Road Surface Temperature Change using Road and Weather Conditions

도로 및 기상조건을 고려한 노면온도변화 패턴 추정 모형 개발

  • 김진국 (한국건설기술연구원 인프라안전연구본부) ;
  • 양충헌 (한국건설기술연구원 인프라안전연구본부, 과학기술연합대학교대학원 도시 및 교통시스템공학과) ;
  • 김승범 (국립경상대학교 건축도시토목공학부) ;
  • 윤덕근 (한국건설기술연구원 인프라안전연구본부, 과학기술연합대학교대학원 도시 및 교통시스템공학과) ;
  • 박재홍 (한국건설기술연구원 인프라안전연구본부)
  • Received : 2018.01.23
  • Accepted : 2018.03.16
  • Published : 2018.04.16

Abstract

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.

Keywords

References

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