# Analyzing Important Elements for Improving the Safety of Motorways
## 3 Data Description and Preprocessing  
Data regarding the frequency of traffic accidents per \"conzone\" on motorways in South Korea, along with 12 influencing factors, were utilized for this study. Here, a conzone denotes the segment between motorway interchanges. The choice of motorways for analysis was deliberate due to the ease of collecting comprehensive data related to traffic accidents compared to other types of roads. Traffic accidents are caused by a wide range of factors (e.g., human, road environment, and vehicle factors). Incorporating a diverse range of data improves the reliability and accuracy of predicting traffic accident occurrences. To this end, we collected relatively recent data covering various types of accident-related factors. The data are categorized into four groups: traffic environment, traffic safety, geometric characteristics, and weather conditions, comprising a total of 12 data types. The temporal scope of the data collection is limited to the year 2021. This selection was made to ensure consistency across the diverse data types while also utilizing the most recent year that allowed for the collection of all relevant data. Unlike existing studies that primarily use variables such as traffic volume or road segment length, we introduced unique and specific \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Previous Research** & **Proposed Method** \\ \hline \multirow{2}{*}{Focus Area} & Identifying key influencing factors & Analyzing the consistency of significant variables derived from each type of model \\ \hline \multirow{4}{*}{Model Types} & Traditional statistical models & \\  & [15; 16; 17; 18] & \\ \cline{2-3}  & AI-based models [19; 21; 22] & Combining the results of two models: Traditional statistical model and AI-based model \\ \cline{2-3}  & Hybrid models [21] & AI-based model \\ \cline{2-3}  & Explainable AI [26; 27] & \\ \hline \multirow{4}{*}{Characteristics} & Traditional statistical models offer interpretability but lower performance & DNN with SHAP provides high performance and enhanced interpretability (e.g., global and local explanations via Beeswarm plots) \\ \cline{2-3}  & AI-based models offer high performance but lack interpretability & interpretability (e.g., global and local explanations via Beeswarm plots) \\ \hline \multirow{4}{*}{Distinction} & \multirow{4}{*}{-} & Applying traditional statistical models and an AI-based model to demonstrate the consistency of key influencing factors \\ \cline{1-1} \cline{2-3}  & & Applying traditional statistical models and an AI-based model to demonstrate the consistency of key influencing factors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of previous research and the proposed method. variables, such as the 'Number of Hazardous Driving Behaviors', which provide a deeper understanding of accident characteristics. These innovative variables, rarely used in prior research, aim to enhance the reliability and accuracy of traffic accident prediction. The 12 influencing factors employed in this study include 'Truck Traffic Ratio', 'Slow Traffic Time', 'Congestion Time', 'Number of Hazardous Driving Behaviors', 'Conzone Length', 'Hazardous Longitudinal Slope Ratio', 'Hazardous Curve Radius Ratio', 'Fog-Prone Section Ratio', 'Ice-Prone Section Ratio', 'Snow-Prone Section Ratio', 'Number of Lanes', and 'Total Traffic Volume', as presented in Table 2.