# Risk Prediction for Winter Road Accidents on Expressways
## 1 Introduction  
The ever-increasing vehicular traffic has resulted in corresponding increase in fatalities due to traffic accidents [1]. In particular, road accidents on expressways are reported to have higher mortality rates than those on other types of roads. Specifically, frozen or snow-covered roads are primarily responsible for fatal and large-scale accidents [2]. During the winter season in the Korean peninsula (with four distinct seasons), the roads are covered with a large amount of ice. In this regard, a service that presents prediction and guidance on the risks of traffic accidents due to weather conditions in winter may reduce the road accidents fatalities. The identification of causes of road accidents is one of the main goals of road accident analysis [3]. The major causes of road accidents include human carelessness, vehicle defects, road conditions, and weather variables. In particular, changes in snowfull, rainfall, and weather have a significant impact on road safety, by reducing the driver's visibility, and the friction between the vehicle and the road [4]. Among these causes, factors such as human carelessness and vehicle defects occur by chance, making it difficult to predict and provide guidance in advance. However, the quantitative prediction of the risk of road accidents due to weather conditions is possible using weather information and road geometry. In this study, we aim to investigate the causes of past traffic accidents on highways in winter and examine the relationship between factors affecting accidents, such as road weather conditions and road geometry using various machine learning models. There has been an active research on traffic accidents and prediction models using a wide range of approaches. [PERSON] predicted the percentage of road accidents and determining factors of accidents using the random forest model, logistic regression model, decisiontree model and k-nearest neighbor algorithms [5]. [PERSON] et al. performed simulations on the prediction of road accidents and their severity using the decision tree, random forest, multilayer perceptron, and naive Bayes models [6]. [PERSON] examined the causes for road accidents using the random forest model and the XGBoost model. The factors considered in the study include traffic volume, road geometry, road surface conditions, and weather information [7]. [PERSON] et al. examined factors influencing accident severity and occurrence using a logistic regression model [8]. Studies on the factors that cause road accidents include, the investigation of the effects of road geometry and weather conditions [9], traffic volume and road friction force in winter [10], and road accident risk owing to weather effects [11]. In this study, factors such as road weather, surrounding altitude, presence of bridges and tunnels, turning radius, and angle of rotation were applied to various machine learning models to predict general road accidents and risks due to weather, and key factors were determined. The Korean Ministry of Land, Infrastructure and Transport (MOLIT) discloses information on road accident occurrence locations with the classification of construction/accident/weather through MOLIT OpenAPI Service [12]. \"Construction\" provides information on areas where traffic control has been applied due to construction work, \"Accident\" provides information on road accident points, and \"Weather\" provides information on the location of road accidents due to weather. However, this service does not provide information other than the location of the road accident, the type of accident, and the time of the accident. The Korea Meteorological Administration (KMA) discloses weather information observed through an automatic weather station (AWS) and an automated synoptic observing system (ASOS) through the KMA website [13]. However, these weather stations are designed to monitor weather changes in the Korean Peninsula and do not directly collect road-level weather information.