# Analyzing Important Elements for Improving the Safety of Motorways
## 6 Conclusions  
This study aimed to estimate traffic accident frequency and identify and assess the consistency of significant variables across different models. To achieve this, both an AI-based model (DNN) and a traditional statistical model (negative binomial) were employed to identify and analyze key factors influencing traffic accident frequency. In particular, the DNN model was enhanced with the XAI methodology SHAP to improve interpretability. The analysis utilized data on traffic accident frequency per conzone on South Korean motorways, along with 12 influencing factors. The DNN model identified five significant factors, including Truck Traffic Ratio (TruckRatio), Congestion Duration (CongTime), Number of Hazardous Driving Behaviors (HazardousBehavior), Conzone Length (ConzLength), and Total Traffic Volume (TotalVolume). Notably, significant variables were selected using \(p\)-values and confidence intervals in the statistical model, while the DNN model identified them using SHAP-derived feature importance. This finding confirms that key factors with significant impacts on traffic accident frequency consistently emerge regardless of model type. For future motorway traffic accident frequency estimation models, these five key variables should be integrated, while additional variables could be tailored to road characteristics, data properties, and model types. This streamlined approach could enhance resource efficiency in model development. In this study, the proportion of truck traffic, the number of dangerous driving behaviors, and the duration of congestion were confirmed as important factors. It is believed that if these factors are well-managed, the frequency of traffic accidents on highways can be effectively reduced. This study addresses the gap between traditional statistical methods and AI-based approaches, offering a precedent for applying XAI in the field of traffic safety. These contributions serve as a foundation for implementing measures to enhance motorway safety by preventing traffic accidents. The main contributions of this study are as follows. One major distinction of this study lies in the utilization of diverse and unique data. Traffic accidents are caused by a wide range of factors, including human factors, road characteristics, and environmental conditions. While previous studies have typically used common variables such as age, gender, road geometry, and weather conditions to predict traffic accidents, this study incor \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **NB (95\% CI)** & **DNN** \\ \hline Truck Traffic Ratio (TruckRatio) & \(\circ\) & \(\circ\) \\ \hline Slow Traffic Time (SlowTime) & & \\ \hline Congestion Time (CongTime) & \(\circ\) & \(\circ\) \\ \hline Number of Hazardous Driving Behaviors & & \\ (HazardousBehavior) & \(\circ\) & \(\circ\) \\ \hline Conzone Length (ConzLength) & \(\circ\) & \(\circ\) \\ \hline Hazardous Longitudinal Slope Ratio & \(\circ\) & \\ (LongSlopeRatio) & & \\ \hline Hazardous Curve Radius Ratio & & \\ (CurveRadiusRatio) & & \\ \hline Fog-Prone Section Ratio (FogProneRatio) & \(\circ\) & \\ \hline Ice-Prone Section Ratio (IceProneRatio) & & \\ \hline Snow-Prone Section Ratio (SnowProneRatio) & & \\ \hline Total Traffic Volume (TotalVolume) & \(\circ\) & \(\circ\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the results of the selection of significant influencing factors between the negative binomial regression model and the DNN model. portates additional variables that provide more precise insights into accident characteristics. Examples of road environment characteristics include Fog/Ice/Snow-Prone Section Ratios, which not only consider weather conditions (e.g., snow, rain, and fog) but also indicate the proportion of road sections prone to such conditions, thereby integrating geometric and weather-related factors.