# Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
## 2 Related Work  
Indeed, there have been numerous scholars who have employed various methods in the field of road traffic accident cause analysis [14]. These methods range from simple statistical analysis of historical accident data to more complex analysis using multi-dimensional variable rough set, grey correlation, and other techniques. While econometric theory has been used in some studies, it has limitations in terms of data completeness and is not widely used. The use of econometric models requires high-quality data, which may not always be available, and they can be sensitive to the choice of variables and the specification of the model. Therefore, the development of data statistics and analysis is limited. In recent years, there has been an increasing interest in using complex network theory or machine learning techniques for traffic accident cause analysis. However, the large amount of redundancy or high dimensionality of the data features cause excessive training time. These methods have been applied to various fields, including ocean-going ship accidents, tunnel stability, road traffic accidents, and three-wheeled motor vehicles. However, the combination of the two approaches has not been effectively explored. In the field of ocean-going ship accidents, [PERSON] et al. used complex network methods to analyze the causes of accidents and identified key factors, such as ship collision, which provided theoretical support for ship navigation safety supervision and risk prevention of marine transportation systems [15]. Factors affecting tunnel stability were sorted out by [PERSON] et al. using TOPSIS, grey correlation analysis, and other analytical methods [16]. In road traffic accidents, machine learning techniques have been widely used to predict the severity of injury. [PERSON] et al. compared over 25 different methods and found that Random Forest was the best method for predicting the severity of injury in road traffic accidents [17]. In the domain of three-wheeled motor vehicles, [PERSON] et al. proposed the use of the Decision Jungle (DJ), Random Forest (RF), and Decision Tree (DT) techniques to investigate the gravity of injuries resulting from traffic accidents, and DJ demonstrated the highest level of accuracy among the three methodologies [18]. Based on complex network theory, [PERSON] et al. established a new model for analyzing the causes of public urban logistics accidents, and analyzed the accidents from a global perspective, identifying the main causes [19]. [PERSON] et al. proposed the use of the Extreme Gradient Boost (XGBoost) method to analyze the influencing factors of pedestrian traffic accidents among the elderly and identified driver characteristics, elderly characteristics, and vehicle movement as the most important factors affecting the severity of accidents [20]. [PERSON] et al. used a combination of Accimaps and STAMP-CAST methods, while also utilizing PCM, to analyze collision investigations at railway intersections. The combination of these methods helps to provide a comprehensive set of safety recommendations [21]. In the study of road traffic accidents, the traditional single-factor, single-type micro-level causal analysis has been replaced by a more comprehensive and multi-faceted approach [22]. This new model takes into account multiple factors and levels of analysis, including the micro and macro levels, to provide a more accurate representation of the complex relationships among various factors that contribute to accidents. The traditional fault tree model, which is a chain structure, is limited in its inability to represent inter-factor correlations and the presence of subjective factors. However, the emergence of complex networks has addressed these drawbacks to some extent. In this paper, a complex network causation model is established from a multi-factor and multi-level perspective of accident occurrence.