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대기기상 데이터를 이용한 야간 노면온도 예측 KCI 등재
Prediction of Nighttime Pavement Temperature Using Atmospheric Data
pp.11-17
PURPOSES : Due to the frequent occurrence of accidents on icy roads during nighttime, it would be advantageous to notify road managers and drivers about the most perilous areas. This would allow road managers to treat the icy roads with de-icing chemicals and enable drivers to be better prepared for potential hazards. Essential information about pavement temperature is required to identify icy spots on the road. METHODS : With the goal of estimating nighttime pavement temperature on the National Highways in Korea using atmospheric data, the current study investigated a widely recognized forecasting method known as deep neural network (DNN). To achieve this objective, the input data for the models were gathered from the weather agency's website. The dataset comprised of relative humidity, air temperature, dew point temperature, as well as the differences in air temperature and humidity between two consecutive days. RESULTS : In order to assess the effectiveness of the built DNN model, a comparison was made using baseline pavement temperature data gathered through an infrared-based pavement temperature sensor installed in a highway patrol car. The results indicated that the DNN model achieved a mean absolute error (MAE) of 0.42 and a root mean square error (RMSE) of 0.62. In comparison, a conventional regression model yielded an MAE of 2.07 and an RMSE of 2.64. Thus, the DNN model demonstrated superior performance in comparison to the conventional regression model. CONCLUSIONS : Considering the increasing focus on preventive maintenance, these newly developed prediction models can be implemented proactively as a preventive measure against icing. This proactive approach has the potential to significantly improve traffic safety on winter roads.
2. 기존 연구
3. 데이터
3.1 데이터 수집
3.2. 노면온도 데이터 특성 분석
4. 야간 노면온도 예측
4.1. 심층신경망 기반 예측모형 구축
4.2. 예측모형 평가
5. 결론
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