# Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
###### Abstract  
The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents' severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents. road traffic accidents; complex network; cause analysis; feature dimensionality reduction; machine learning +
Footnote †: journal: Medical  
address this pressing issue [7]. With the rapid advancement of contemporary technologies, data analysis, data mining, machine learning, deep learning, and other technologies have become powerful tools for analyzing accidents' causative factors, gaining insight into potential laws, and correctly classifying accidents [8]. Traffic accidents are unpredictable and uncontrollable events that pose significant challenges for traffic management departments. The analysis of accidents' causes often relies on expert judgment, which can be subjective and prone to errors. With the increasing complexity of traffic networks in the 21 st century, the accident causal network has become more intricate, with higher interactive complexity, dynamic complexity, structural complexity, and nonlinearity [9]. To address these challenges, complex network theory provides a new perspective for analyzing the internal relations and topology of the network. Complex networks integrate the edges connected between nodes in the network, revealing the underlying relationships among different factors and facilitating the analysis of the interactions among multiple factors in traffic accidents [10]. By reducing the dimensionality of the original data, complex networks can effectively solve the problem of feature redundancy and dimension disaster caused by a large number of data features. Previous studies on traffic causation analyses based on complex networks mainly focused on node metrics, without conducting in-depth analysis of the downgraded features [11; 12]. However, these features hold significant research value and can be utilized to improve road traffic safety and accident collision prediction. The innovation of this article lies in that first, after analyzing the causes of accidents based on complex networks, the most influential accident factors were identified. At the same time, these factors were used as dimensionality reduced features. Finally, a machine learning-based traffic accident injury severity classification model was constructed using dimensionality reduced features. This paper combines complex network theory with machine methods to reveal a new approach that combines complex network principles with machine learning methods. Through relevant experiments, it has been proven that this method can solve the problem of high feature dimensionality leading to longer classification time for accident injury severity. At the same time, the method proposed in this article effectively utilizes the dimensionality reduction features after accident analysis, avoiding the problem of ineffective feature utilization.