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http://dx.doi.org/10.7470/jkst.2014.32.6.686

Development of a Surface Temperature Prediction Model Using Neural Network Theory  

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)
Publication Information
Journal of Korean Society of Transportation / v.32, no.6, 2014 , pp. 686-693 More about this Journal
Abstract
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.
Keywords
neural network theory; pavement sensors; road surface temperature; road management; road weather information;
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Times Cited By KSCI : 2  (Citation Analysis)
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