# Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
## 5 Conclusions  
In this paper, data on road traffic accidents in various states of the United States in recent years were selected to construct an accident causation analysis model based on complex networks. Each feature indicator in the model was extracted and analyzed, and an accident classification model was constructed based on the features after dimensionality reduction. The following conclusions were drawn:  
(1) The complex network model for road traffic accident causation analysis belongs to the scale-free network structure. In the network the nodes are clustered to a higher degree, while the degree of distribution of the nodes is not uniform, and the clustering phenomenon is more obvious. The nodes are more closely connected and have a faster propagation speed; it is easier to connect with other nodes through one node. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method Model** & **Evaluating Indicator** & **Ordinary Accidents** & **Serious Accidents** & **Major Accidents** \\ \hline Logistic & Precision & 0.68 & 0.54 & 0.13 \\ Regression (LR) & Recall & 0.97 & 0.08 & 0.02 \\  & F1-score & 0.80 & 0.14 & 0.04 \\ \hline Decision & Precision & 0.73 & 0.56 & 0.12 \\ Tree (DT) & Recall & 0.87 & 0.36 & 0.00 \\  & F1-score & 0.80 & 0.44 & 0.00 \\ \hline Random & Precision & 0.74 & 0.53 & 0.19 \\ Forest (RF) & Recall & 0.83 & 0.41 & 0.05 \\  & F1-score & 0.78 & 0.46 & 0.07 \\ \hline Extreme Gradient & Precision & 0.74 & 0.56 & 0.25 \\ Boosting (XGBoost) & Recall & 0.86 & 0.38 & 0.01 \\  & F1-score & 0.80 & 0.45 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation indexes of different machine learning methods on different accident categories. (2) Through the extraction and analysis of node indicators in the network, traffic management factors are the main influencing factors of road traffic accidents. Key factors influencing the occurrence of accidents include no intersections on the road, no deceleration signs, no railways, and no safety measures. At the same time, a comprehensive importance evaluation model of complex networks for accident causation analysis was constructed. It was found that having more input indexes in the model did not necessarily make its performance better. When the node degree, intermediary centrality, and closeness centrality were used as the input of the model, the performance of the model was better. (3) Four different machine learning classification models were constructed using feature covariates after dimensionality reduction by the complex network method as inputs. By comparing the precision rate, recall rate, F1-score value, and sensitivity test of the models, it was found that the performance of Extreme Gradient Boosting (XGBoost) was better than the other three models. (4) Other categories of influencing factors could be included in subsequent studies, such as driver characteristics, vehicle characteristics, and traffic flow, which can increase the intrinsic characteristics of the complex network and the training dimension of the model, and can better demonstrate the specific environment in which accidents occur. At the same time, other classes of machine learning or deep learning methods can also be applied. Innovating on the original and analyzing the few most influential features is a way to reduce the cost of model training while improving model performance. Conceptualization, Y.W. ; methodology, Y.W. and H.Z. ; validation, X.C. and X.G. ; investigation, H.Z. ; writing and editing, Y.W. and H.Z. All authors have read and agreed to the published version of the manuscript. This paper was supported by the Doctoral Program of Zhengzhou University of Light Industry (2021 BSJ)047). Not applicable. The datasets we use are all open public data benchmarks, and the relevant addresses are: [[https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents](https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents)]([https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents](https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents)).