# Risk Prediction for Winter Road Accidents on Expressways
## 6 Conclusions  
This study evaluated the risks of traffic accidents on highways in winter by classifying accidents into those caused specifically by the weather and general road accidents. As the weather information on the highways could not be directly obtained, the wind speed and humidity were predicted using the road-level weather data by spatial interpolation of the data of the surrounding weather stations. To determine the risk of road accidents on highways, the angle of rotation and turning radius were calculated using the Douglas-Peucker algorithm and standard node links, and the road with the largest angle of rotation was selected as the representative point of each link. Using SRTM data, the difference between road altitude and that of the surrounding area and shadow information were produced to construct the combined data. To verify the prediction performance of the model, the combined data were divided into training data and test data in a 7:3 ratio. The data imbalance problem was resolved using the SMOTE algorithm for the training data, and 10 combined databases were constructed. Random forest, XGBoost, ANN model, and logistic regression model were used as machine learning models to predict the risk of accidents due to weather, and random forest was selected as the optimal model  
Figure 4: Risk of road accidents on highways caused by weather in Korea.  
Figure 3: Location of Korean highways, angle of rotation by link, humidity by link.  
(accuracy = 0.984, kappa = 0.658, AUC = 0.907, F-measure = 0.992, G-mean = 0.904). As a result of identifying important factors through the random forest model and the XGBoost model among the CART-based models, the important variables for predicting road accident risks due to weather were identified to be (in increasing order of importance) humidity, geographic location (latitude, longitude), time, and road geometry information. Finally, the visualization service enabled an easy understanding of the conditions for the road users and researchers, by means of various information on road accidents in the winter season.  
This study had several limitations. This study predicted weather information using spatial interpolation because it was not possible to collect direct weather information on highways. As accurate road weather information cannot be obtained, there is uncertainty in the accuracy of predicting accidents caused by weather. Accidents due to the slipperiness of the road are affected by road material, road traffic volume, driving speed, road slope, and road surface temperature. Therefore, research on winter road risk prediction reflecting these various factors remains a future challenge.  
D.K.: methodology, visualization, analysis, writing; S.J.: collecting data, methodology; S.Y.: conceptualization, writing original draft preparation, review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Daegu University Future Scholars program, 2021.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors would like to express their gratitude to all those who contributed to this study.  
The authors declare no conflict of interest.