Risk Prediction for Winter Road Accidents on Expressways

Road accidents caused by weather conditions in winter lead to higher mortality rates than in other seasons. The main causes of road accidents include human carelessness, vehicle defects, road conditions, and weather factors. If the risk of road accidents with changes in road weather conditions can be quantitatively evaluated, it will contribute to reducing the road accident fatalities. The road accident data used in this study were obtained for the period 2017 to 2019. Spatial interpolation estimated the weather information; geographic information system (GIS) and Shuttle Radar Topography Mission (SRTM) data identified road geometry and accident area altitude; synthetic minority oversampling technique (SMOTE) addressed the data imbalance problem between road accidents due to weather conditions and from other causes, and finally, machine learning was performed on the data using various models such as random forest, XGBoost, neural network, and logistic regression. The training- to test data ratio was 7:3. Random forest model exhibited the best classification performance for road accident status according to weather risks. Thus, by applying weather data and road geometry to machine learning models, the risk of road accidents due to weather conditions in the winter season can be predicted and provided as a service.