대한교통학회지 (Journal of Korean Society of Transportation)

  • 제32권6호
  • /
  • Pages.686-693
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  • 2014
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  • 1229-1366(pISSN)
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  • 2234-4217(eISSN)
대한교통학회 (Korean Society of Transportation)
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신경망 이론을 이용한 노면온도예측모형 개발

Development of a Surface Temperature Prediction Model Using Neural Network Theory

  • 김인수 (한국건설기술연구원 도로교통연구실) ;
  • 양충헌 (한국건설기술연구원 도로교통연구실) ;
  • 최기주 (아주대학교 교통시스템공학과)
  • Kim, In Su (Highway & Transportation Research Division, Korea Institute of Civil Engineering and Building Technology) ;
  • Yang, Choong Heon (Highway & Transportation Research Division, Korea Institute of Civil Engineering and Building Technology) ;
  • Choi, Keechoo (Department of Transportation System Engineering, Ajou University)
  • 투고 : 2014.08.21
  • 심사 : 2014.10.03
  • 발행 : 2014.12.31
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초록

본 연구에서는 도로기상정보체계에서 습득할 수 있는 노면온도자료를 활용하여 신경망 이론을 통해 노면온도를 예측하는 모형을 개발하였다. 이를 위해 수집된 노면온도자료(노면온도, 대기온도, 대기습도)를 가지고 1시간, 2시간, 그리고 3시간 후의 노면온도를 예측할 수 있는 신경망을 설계하였다. 청원-상주간 고속도로를 대상으로 모형을 수행한 결과, 예측치와 관측치에 대한 편차의 표준편차가 1시간 예측인 경우 $0.55^{\circ}C$, 2시간 예측인 경우 $1.27^{\circ}C$, 3시간 예측인 경우 $1.43^{\circ}C$를 나타났다. 또한 예측된 노면온도를 실제 관측한 자료와 비교한 결과 R2 값이 각각 0.985, 0.923, 0.903으로 나타나 모형의 설명력이 높은 것으로 판단된다.

This study presents a model that enables to predict road surface temperature using neural network theory. Historical road surface temperature data were collected from Road Weather Information System. They used for the calibration of the model. The neural network was designed to predict surface temperature after 1-hour, 2-hour, and 3-hour from now. The developed model was performed on Cheongwon-Sangju highway to test. As a result, the standard deviation of the difference of the predicted and observed was $1.27^{\circ}C$, $0.55^{\circ}C$ and $1.43^{\circ}C$, respectively. Also, comparing the predicted surface temperature and the actual data, R2 was found to be 0.985, 0.923, and 0.903, respectively. It can be concluded that the explanatory power of the model seems to be high.

키워드

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