## 4 Results
### Derivation of Promising Evaluation Indicators and the Integrated Evaluation Index  
A DT analysis was conducted to derive the promising evaluation indicators that should be used to identify hazardous sections on actual roads from among the candidate interaction safety evaluation indicators. In establishing the DT model, simulation-based interaction safety evaluation indicators were used as the input variables; the output variables were the DRI-based hazardous road sections and normal sections defined using AVD. All scenarios were analyzed with a 7:3 ratio of training data to test data, with a total of 186 data points. The hyperparameters of the model were determined from this analysis. The definitions of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Descriptive Statistics**} \\ \hline
**Statistics** & **N** & **Avg.** & **Std.** & **Var.** & **85 th\%** & **Range** & **Min.** & **Max.** \\ \hline Deceleration for risk event & 9658 & \(-\)0.798 & 0.726 & 0.527 & \(-\)1.111 & 13.833 & \(-\)13.889 & \(-\)0.056 \\ \hline DRI by section & 186 & 0.338 & 0.947 & 0.896 & 0.464 & 5.708 & 0 & 5.708 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics of deceleration for risky event and DRI by section. Figure 2: Example of DRI-based hazardous road section. the parameters and the optimized values of the parameters for each scenario are presented in Table 5[30]. The confusion matrix and information gain of MPR 20 are shown in Table 6 as an example of the results of the decision tree analysis. The MPR 20 scenario resulted in a classification accuracy of 87.50%, while the MPR 40, 60, and 80 scenarios resulted in 78.57%, 85.71%, and 80.36% accuracies, respectively. The number of promising indicators that affected the classification of hazardous sections varied by scenario. First, four promising indicators of MPR 20 and their respective information gains are as follows. The average spacing was 0.359, the SDI-based conflict count was 0.332, the time-varying volatility of headway was 0.270, and the standard deviation of headway was 0.039. These values were used as weights to develop an integrated index. Second, three promising indicators were derived for MPR 40, namely, the average spacing, TTC-based conflict count, and headway-based time-varying volatility, which were used as weights to develop the integrated evaluation index. For MPRs of 60 and 80, the same method was used to develop the integrated evaluation index. The formulas for the integrated evaluation indices for the scenarios with MPRs of 20, 40, 60, and 80 are presented in Equations (9), (10), (11), and (12), respectively. The average spacing was found to be a promising indicator and was included in the integrated index in all scenarios. Additionally, it was the indicator that best identified hazardous road sections among the candidate evaluation indicators for each scenario. Regardless of the MPR of autonomous vehicles, spacing is an indicator that affects driving safety; the shorter the distance is, the more hazardous driving is. The developed integrated evaluation indices were utilized to derive hazardous road sections for each scenario.