# Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model
## 2 Literature Review
The attributes of the age of the injured, age of the driver, vehicle model, vehicle class (truck, SUV, car, etc. ), cause of the accident, road type, speed limit on the road, type of accident, lighting, intersection, using a belt, car brand, gender of the injured, and gender of the driver were all found to have an effect on accident severity [11]. In a separate study conducted in Iran, fatal accidents were investigated through a Logic Regression (LR) model with an applied cross validation method to avoid overfitting. The study identified road lighting, speed limit, and compliance with traffic rules as the important factors in accident severity [12]. In a study carried out in Seoul, XGBoost (XGB), Logistic Regression (LR), and DBSCAN, machine learning methods, were used to estimate variables affecting accident severity. The study points out that weather, season, and day of the week do not significantly affect accident severity. Since pedestrian-related features come to the forefront the most, it is emphasized that measures to be taken for pedestrians should be increased [13]. The accident severity level was modelled in another study conducted in China through Random Forest by using a dataset created from video recordings. Key features identified as having a significant effect on accident severity included accident type (such as pedestrian-vehicle or motorcycle-truck collision, etc. ), engine capacity, collision speed, speed limit, road information, accident location, vehicle manoeuvre, and vehicle age [14]. CHAID decision tree and Bayesian Network Analysis (BN) data mining techniques were used to reveal the attributes that are effective at predicting the severity of bicycle accidents in Italy. The best predictive factors revealed by CHAID analysis are, in order of their proportional effects, road type, accident type, age of cyclist, road signage, gender of cyclist, type of opposing vehicle, month of the year, and type of road segment. In the Bayesian network analysis, the factors that best predicted the severity of the accident in bicycle accidents were accident, road, and collision vehicle type [15]. CHAID and Bayesian Network techniques, together with path analysis, were applied to determine different combinations of accident factors in roadside accidents. Vehicle speed,curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope were found to be the most important factors, respectively [16]. A Multinomial Logistic Regression (MLR) model, Artificial Neural Network Multilayer Perceptron (ANN-MLP), Chi-square Automatic Interaction Detector (CHAID), and C5.0 were used to analyse crash severity in a study conducted with data from the California HSIS database for all state highways comprising crash information for the years 2012-2014. The study revealed that the cause of the crash, the number of vehicles involved, the weather, and the driver's age factors were important [17]. [PERSON] and [PERSON] used Binary Logit (BL) and CHAID methods to reveal the relationships between injury severity and various factors in crashes. Their study found that drivers and pedestrians were more likely to be seriously injured or killed than passengers. Additionally, crashes occurring on weekends, at noon, and at night were found to increase the probability of the crash being fatal or severe [18]. [PERSON] et al. employed C5.0, CHAID, and CART algorithms to determine the factors affecting the severity of the side right-angle collision type of accident. Drunk driving, weather conditions, excessive speed, the effective speed of head-on vehicles, and traffic light equipment were obtained as significant factors. The study also found that the occurrence of serious accidents increased with the rising effective speed of head-on vehicles and decreased when road isolation facilities were installed. In addition, it was revealed that there was a high probability of serious accidents in rainy/snowy conditions.