## 5 Modeling Results  
### Data Set  
In the models, the original data set (a sample of 518 subjects, as described in the previous section) was divided into two parts: a training data set and a test data set. The model was developed by training the classifier using the training data set, and the performance of the classifier was tested on the test data set. For this study, 75% of the data were utilized in the training data set, and the rest were used as the test data set. Since the process of assigning data can influence the model outcomes, the cases were randomly assigned to the training and test data subsets.  
Before evaluating the models, the out-of-bag estimate of error rate (OOB) of each model was calculated to identify the optimal number of trees for model evaluation. The OOB is the mean prediction error on each training sample \(x_{i}\) determined using only the trees that do not have \(x_{i}\) in their bootstrap sample [58].  
In this study, 500 trees were used and were divided into samples of five sizes, \(N\) = {100, 200, 300, 400, 500}, for simulation. For each data point (\(x_{i}\), \(y_{i}\)), the classifiers built on a bootstrap sample that did not contain (\(x_{i}\), \(y_{i}\)) were retained and aggregated. Then, the predicted labels were compared to obtain the final value, \(y_{i}\). After performing these steps for each data point (\(x_{i}\), \(y_{i}\)) of the learning set, the OOB for each point was calculated as the mean of the OOB over 10 runs to avoid insignificant-sampling effects. The resulting OOB was 4%. Figure 6 illustrates the OOB, showing that both curves tend to stabilize when the number of trees exceeds 200.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Abbreviation** & **Description** & **Category Coding/Value** \\ \hline \multirow{3}{*}{Accident} & \multirow{3}{*}{AL} & \multirow{3}{*}{Accident level} & Range of 0.08–1.88 \\  & & & _One fatality has a weight of 1.2_ \\  & & & _one non-fatal injury has a weight of 0.3_ \\ \hline \multirow{6}{*}{Road alignment factors} & \multirow{3}{*}{SV} & \multirow{3}{*}{Vertical grade} & 0 = Curve \\  & & & 1 = Tangent \\  & & & & Range of -4.3–10.8\% \\  & & & & \({}^{*}\)_Mean_: 0.01\% \\  & & & & Range of -4.1–4.1\% \\  & & & & \({}^{*}\)_Mean_: 0.01\% \\  & & & & Range of 180–3550 m \\  & & & & \({}^{*}\)_Mean_: 625 m \\  & & & & Range of 180–760 m \\  & & & & \({}^{*}\)_Mean_: 393 m \\ \hline \multirow{3}{*}{Weather factors} & \multirow{3}{*}{RI} & Rainfall intensity & Range of 0.5–56 mm/h \\  & & & & \({}^{*}\)_Mean_: 3.7 mm/h \\  & & & & \(0\) = Dry \\  & & & & \(1\) = Wet \\ \hline \multirow{3}{*}{Road environment factors} & \multirow{3}{*}{VT} & \multirow{3}{*}{Vehicle type} & 1 = Small passenger car \\  & & & 2 = Large passenger car \\  & & & & \(3\) = Other \\  & & & & \(0\) = Night \\  & & & & \(1\) = Day \\ \hline \multirow{3}{*}{Human factors} & \multirow{3}{*}{Da} & \multirow{3}{*}{Driver age} & Range of 19–81 years \\  & & & & \({}^{*}\)_Mean_: 43 years \\ \cline{1-1}  & & & & \(0\) = Male \\ \cline{1-1}  & & & & \(1\) = Female \\ \hline \hline \end{tabular} NOTE: 1 m = 3.28 ft; 1 mm = 0.0394 in.  
\end{table}
Table 1: Variable descriptions.