# Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model
###### Abstract  
The number of traffic accidents in a region rises as the vehicle-km value in traffic increases. Furthermore, since automobiles make up the highest proportion of vehicles in traffic, they represent the greatest weight in traffic accidents. This study aims to establish a model to predict the driver's status (survived-injured-dead) as a result of the fatal-injury type of accident. The size of the vehicles suppresses the direct factors related to drivers by having a significant and dominant effect on the analysis of the results of the accidents by concealing the other important factors which must be taken into consideration with regard to the casualty levels of the drivers. Consequently, this paper focuses on automobiles, which are the most frequently involved vehicle type in accidents. Furthermore, the dataset representing the accidents that occurred in Turkey between 2015 and 2021 was employed for the analysis of the effects of the attributes of the drivers on the outcome of casualties for automobile-related accidents alone. The uniqueness of this research stems from being the first study in Turkey to investigate the severity levels of the drivers involved in automobile-related accidents. In addition, this study highlights the preventable factors investigated relatively less than other factors in the literature in order to establish a successful model. The difference between the success of the models with regard to accuracy obtained through dominant and investigated factors is only 5.0%. Random Forests, Naive Bayes, and CHAID (Chi-squared Automatic Interaction Detection) models were established and compared as decision tree algorithms. The results revealed the fact that the CHAID model produced the most successful outcomes among them. Driver fault, gender, education level, and age, along with alcohol usage and surface condition, were found to be significant influential factors for the severity of traffic accidents. 1  
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Z.C. or uninvolved passengers. This uncertainty with regard to this remarkable proportion leads the drivers' conditions to be taken into consideration only for the analysis pertaining to the data obtained from police accident records. Another important point to be mentioned regarding why only the drivers were paid attention in this study is that accident reports in Turkey reveal the fact that drivers' faults represent 90% of the causes of the accidents. This clearly highlights that drivers' characteristics and faults in the accidents have to be examined to properly determine the impact of the related drivers' attributes. In this context, single, two-vehicle, and multi-vehicle car accidents are examined to identify key factors that influence accident severity for drivers by mainly focusing on the automobile-related accidents since they prevail in the general dataset contents. Furthermore, heavy vehicle-involved accidents dominantly characterise the severity of the accidents concealing the leading effects of primary parameters. In addition, as the police in Turkey record only accidents involving fatalities or injuries, not those involving property damage alone, the dataset in this research includes \"fatal\" and \"injury\" cases. On the other hand, as the dataset has an overwhelmingly overbalanced distribution of fatal and injury accident rates of 0.7% and 99.3%, respectively, it was challenging to predict the accident outcome by classifying it as fatal or injury. Therefore, the evaluation of the accident severity was based on the conditions of the casualties as the outcome of the accident. Since police reports classify conditions of the drivers, passengers, and pedestrians into three categories, there are three designated classes to describe the status of individuals involved in the accidents: \"dead\", \"injured\", and \"uninjured\". Many studies have been conducted to develop a prediction model to reveal the significant factors affecting traffic accident severity [3; 4; 5; 6; 7].