# Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model
## 2 Literature Review  
In Taiwan, Taipei, the CART model was employed to determine the relationships between injury severity and road-environment/driver-vehicle characteristics along with accident attributes. The outcomes of the study revealed the fact that the strongest correlation was between vehicle type and injury severity. Another finding of the research was that pedestrians, motorcyclists, and cyclists had a higher risk of injury than other vehicle drivers [3]. The CART method was used in other research to determine the factors affecting the accident severity of back-seat motorcycle passengers in Iran. It has been concluded that the residential or rural nature of the accident location, land use, and injured part of the body (head, neck, etc.) are the most influential factors affecting the fatality of motorcycle passengers [4]. Classification Trees (CT) analysis and Association Rules Mining (ARM) were employed in a study analysing powered two-wheeler (PTW) accidents in Italy. These accidents were found to be strongly sensitive to the various combinations of road, environment, and drivers' attributes. While one of the outcomes of the research was that accidents in rural areas exhibited a higher accident severity, the other important conclusion stated the critical importance of the consistency of the grip of the types on the road surface for the stability of the PTWs. This is particularly important due to the fact that these types of vehicle have only two points of road surface contact compared to four-wheel vehicles' four contact points [5; 8]. In a study conducted in Maryland, USA, multinomial logit (MNL) and random forest models were established to identify factors affecting the level of severity of the accidents. The major contributing factors obtained were collision type, age of the occupants, and the speed limits [6]. The machine learning methods of Decision Jungle (DJ), Random Forest (RF), and Decision Trees (DT), which were used to predict the injury severity in three-wheel rickshaw accidents in Rawalpindi, Pakistan, identified accident type, weather condition, accident cause, posted speed, lighting conditions, and age of the drivers as the leading attributes in predicting accident severity. Among them, the Decision Jungle method performed as the best prediction method when overall accuracy values were compared [7]. In a study carried out in Alabama, USA, the aim was to estimate the severity of accidents involving young and old drivers in lane-changing-related accidents. According to the results of the Random Logit (RL) model conducted in the study, it was found that young male drivers were involved in more severe injuries than older male drivers. It has been observed that while young male drivers were inclined to get involved in major injury-level accidents rather than minor or no injury ones in daylight conditions, older male drivers tended to have the same injuries in dark/unit driving and road conditions. It has also been found that driver errors, such as not wearing seat belts, impaired driving (alcohol or drug use), and driving without a valid license are linked to severe injuries [9]. Estimation models in a study in Cyprus were established using machine learning methods such as Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), and Artificial Neural Networks (ANN) by classifying the accident severity in two classes as serious and non-serious. The effects of attributes on accident severity were listed with Random Forest feature importance. The outcomes of this study state that the vehicle engine power, vehicle age, driver age, the road being a first-degree major road, the presence of an intersection, and the available speed limit have an effect on accident severity, respectively [10]. In a study conducted in the United Arab Emirates, machine learning methods such as Gradient Boosting (GB), Support Vector Machines (SVM), and Random Forest (RF) were used to determine accident severity as mild, moderate, severe, or fatal.