Retrieval of Road Surface (Bridge Deck) Temperature near 0 °C Based on Random Forest Model

Abstract

Based on the road surface (bridge deck) temperature, relative humidity, air temperature, wind speed and precipitation observed at two road surface meteorological stations and two bridge deck meteorological stations, as well as subsurface temperature at different depths observed at Hefei meteorological station, the independent variables are selected to establish the relationship between these factors and road surface temperature, using random forest and stepwise regression. The performance of these two methods was compared, and the importance of each factor was analyzed. Results show that the road surface (bridge deck) temperature linearly correlates with air temperature. In the case of low air temperature conditions (air temperature ≤ 8 °C), the road surface temperature is mainly higher than air temperature observed at the same station, and the bridge deck temperature is mainly lower than air temperature. In the retrieving of road surface temperature and bridge deck temperature, the random forest algorithm has lower mean absolute error (MAE) and root mean square error (RMSE) than the stepwise regression algorithm, especially in the retrieving of road surface temperature. MAE of road surface temperature retrieved by random forest on two bridge deck stations is reduced by 0.19 °C and 0.26 °C compared with the stepwise regression, and RMSE is reduced by 0.33 °C and 0.49 °C, respectively. The bias in the retrievals can be originated from the model itself and the error in the observations. Among the factors in the random forest model, air temperature is the most important. Meanwhile, there are differences in the importance of each factor in the retrieval of road surface temperature and bridge deck temperature. The subsurface temperature is more important in retrieving road surface temperature, while humidity and wind speed are generally more important to bridge deck temperature. It should be noted that due to the limitation of the observations, this study did not consider the net radiative flux, and the influence of net radiative flux on bridge deck and road surface temperature may be different.

Keywords

• road surface temperature retrieval
• bridge deck temperature retrieval
• random forest model
• stepwise regression