한국도로학회논문집 (International Journal of Highway Engineering)

  • 제19권2호
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  • Pages.35-44
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  • 2017
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  • 1738-7159(pISSN)
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  • 2287-3678(eISSN)
한국도로학회 (Korean Society of Road Engineers)
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기계학습을 이용한 노면온도변화 패턴 분석

Analysis of Road Surface Temperature Change Patterns using Machine Learning Algorithms

  • 양충헌 (한국건설기술연구원 도로연구소, 과학기술연합대학원대학교 교통물류 및 ITS공학과) ;
  • 김승범 (국립경상대학교 건축도시토목공학부) ;
  • 윤천주 (한국건설기술연구원 도로연구소) ;
  • 김진국 (한국건설기술연구원 도로연구소) ;
  • 박재홍 (한국건설기술연구원 도로연구소) ;
  • 윤덕근 (한국건설기술연구원 도로연구소, 과학기술연합대학원대학교 교통물류 및 ITS공학과)
  • 투고 : 2017.01.19
  • 심사 : 2017.03.31
  • 발행 : 2017.04.17
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초록

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

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참고문헌

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