## 3 Results  
### Machine Learning Classification Performance  
The final dataset contains 998 segments with 42 variables, including the dependent variable \"Traffic Crash\". \"Traffic Crash\" is a binary feature that describes whether the accident occurred or not. Table 4 shows the evaluation metrics of using statistical and machine learning methods to classify the dependent variable. It can be seen that all metrics are high. However, in this research, classifying accident-caused cases is more important than classifying not-caused cases. Since the analysis aims to figure out the main variables of traffic crashes, the classification model must perform well in classifying accident-caused cases. The above models have poor performance in classifying accident cases, so we use resampling techniques. Resampling techniques are used to overcome the imbalance problem. Table 5 indicates the overall performance measures of the methods after resampling. After applying the resampling techniques, it seems that the overall classification evaluation metrics decrease. Nevertheless, it is possible to see that the capability to classify accident cases has improved. Resampling techniques significantly improved the ability of models to classify minority-class (accident) cases. For instance, XGBoost with ENN achieved a specificity of 84.3%, indicating its effectiveness in identifying non-accident segments accurately. While overall accuracy decreased slightly due to resampling, the trade-off was acceptable, given the improved recall and precision for accident cases. This demonstrates the importance of prioritizing balanced predictive performance when dealing with highly imbalanced datasets, as shown in Table 5.  
Hyperparameter optimization was performed to obtain better results. In this paper, we use BO for HPO. In previous studies, Grid Search and Random Search were normally used for HPO. Since BO uses a surrogate model by reflecting prior information, a more efficient analysis is possible than that of GS and RS. For applying BO, the GP and TPE are used for the surrogate model. Moreover, to improve the capability to classify accident cases, each HPO model's precision and f1 score is calculated. Table 6 shows the finally obtained results. In this table, it is possible to see highly performed metrics with adequate specificity. When compared with the initial measurement without resampling and HPO, we could find more appropriate results in Table 6. Therefore, we use this model in the result analysis and machine learning interpretations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Metrics**} & \multirow{2}{*}{**Specificity**} \\  & **Accuracy** & **Recall** & **Precision** & **F1 Score** & \\ \hline Logistic Regression & 0.8333 & 0.8435 & 0.9841 & 0.9084 & 0.4 \\ \hline Support Vector Machine & 0.84 & 0.8469 & 0.9881 & 0.9121 & 0.5 \\ \hline RandomForest & 0.84 & 0.8464 & 0.9841 & 0.9101 & 0.5 \\ \hline Gradient Boost & 0.8333 & 0.8482 & 0.9761 & 0.9077 & 0.4 \\ \hline XGBoost & 0.8267 & 0.8425 & 0.9762 & 0.9044 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results without data augmentation. Figure 3 shows the feature importance, indicating which variables were important in the classification process of the model. In Figure 3, it is possible to find out that volume, land use (farmland area), and length are the top three most important variables in the classification process. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Machine Learning Method** & **Sampling Method** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **Specificity** \\ \hline XGBost & ENN & 0.8433 & 0.8451 & 0.996 & 0.9144 & 0.6667 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of machine learning model after hyperparameter optimization.