## 1 Introduction  
Traffic accidents have emerged as a serious social problem today, as the number of car registrations has increased rapidly owing to global economic growth and improvements in living standards [1, 2, 3]. According to the report published by the World Health Organization (WHO) [4] in 2018, nearly 1.35 million people worldwide die in traffic accidents every year, implying that one person dies in a traffic accident every 24 s, an increase of 100,000 people compared to 2015. In addition, according to the Center for Disease Control and Prevention (CDC) [5], the cost of medical and productivity losses associated with deaths from car accidents in one year exceeds $63 billion. Therefore, it is necessary to identify major factors and types of traffic accidents to prevent traffic accidents in advance based on the results obtained.  
Along these lines, a number of related studies and policies are being carried out abroad. However, there is still a lack of understanding of the major causes and mechanisms of serious traffic accidents in Seoul. Seoul is the largest city in South Korea, with various types of transportations used by almost 10 million citizens and vehicles every day, implying that the traffic accidents would cause tremendous social and economic losses.  
The results of traffic accident data analysis may vary depending on the characteristics of the local traffic environment. Thus, it is necessary to focus on the intrinsic properties of Seoul for a deeper understanding of the causes and mechanisms of traffic accidents in Seoul. Furthermore, traffic accidents are caused by a combination of various factors such as human-errors, road conditions, and environments. This means that we need to perform a comprehensive analysis of traffic accident datasets. Additionally, there is no single method that always yields the best results in all cases; therefore, various methodologies with different philosophies should be used for complex analysis.  
In this study, we aim to identify the significant factors and types that affect the severity of traffic accidents by focusing on the cases of Seoul. To this end, we used big data on traffic accidents in Seoul pertaining to various factors by adopting three widely used machine learning techniques: ensemble-based, regression-based, and clustering-based methodologies. Throughout the analysis, we found that the severity of traffic accidents is mainly determined by pedestrian-related variables, not by driver-related variables, which is different from the results reported in previous studies [6, 7, 8, 9, 10]. We assume that this is because of the unique characteristics of Seoul, which has created a vehicle-oriented transportation environment that has been inevitably promoted by the daily traffic volume being so high, almost 10 million vehicles [11].  
This paper makes the following contributions.  
* We analyzed a set of features that affect the number of traffic accidents by classifying the features into three main factors--human, road, and environment--with a focus on Seoul, the capital of Republic of Korea.
* We unveiled the significant features that affect the severity of traffic accidents by exploiting various machine learning approaches: ensemble, regression, and clustering-based analytics.
* By performing further qualitative analysis, we suggest that establishing more preventive measures against pedestrian accidents would be an adequate approach to reduce the number of fatal injuries due to traffic accidents in Seoul.  
The remainder of this paper is organized as follows: Section 2 reviews previous research. Section 3 describes the characteristics of the dataset we used for the analysis. Section 4 introduces three methods that we adopted, and Section 5 shows our findings based on the analysis using these methods. Finally, Section 6 concludes the study.