# Impact of Putting on Traffic Safety: A Synthesis of Research Findings
## 3 Results and Discussion
### Prediction Model
Data imbalance is a well-documented challenge in developing machine learning models for crash-related predictions [57]. Moreover, most of the data points represent highway roads, as shown in Figure 10d. A significant portion of the data corresponds to undefined traffic volumes and weather conditions, which are labeled as \"mixed\", as presented in Figure 10e and f, respectively. These limitations in the dataset must be addressed in future research to develop more reliable models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Study** & **Number of Data Points** \\ \hline [PERSON] et al. [11] & 8 \\ \hline [PERSON] et al. [15] & 9 \\ \hline [PERSON] [52] & 6 \\ \hline [PERSON] et al. [19] & 18 \\ \hline [PERSON] et al. [10] & 20 \\ \hline [PERSON] and [PERSON] [43] & 36 \\ \hline [PERSON] and [PERSON] [56] & 11 \\ \hline [PERSON] et al. [41] & 4 \\ \hline [PERSON] et al. [14] & 33 \\ \hline [PERSON] et al. [13] & 6 \\ \hline [PERSON] et al. [45] & 16 \\ \hline Total & 167 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sources and number of data points collected for this study. Figure 10: Frequency distribution of the data for (**a**) rut depth, (**b**) accident rate, (**c**) country of the study, (**d**) road type, (**e**) traffic volume, and (**f**) weather conditions. The dataset was divided into two subsets for model development and testing. The first subset comprised 152 data points, representing approximately 90% of the total dataset, and was used for model development. The second subset included 15 data points, accounting for about 10% of the dataset, and was used for model testing. To minimize the risk of overfitting during model development, ten-fold cross-validation was employed. Four models were developed: DT, RF, ANN, and SVM. The performance of these models was evaluated during both the training/validation phase and the testing phase using root mean square error and absolute error as metrics. Figure 11a compares the performance of four prediction models: DT, RF, ANN, and SVM. The models' performance was evaluated using root mean square error and absolute error. As shown in Figure 11a, both DT and RF models exhibited fair prediction performance considering the two indicators. The highest performance was attributed to the DT model with root mean square error and absolute error measured as 21.7 and 12.1 accidents per 100 MVkm, respectively. However, it is essential to note that such absolute error may not be acceptable in countries with low accident rates, as discussed earlier. The ANN and SVM models exhibited poor performance, with the ANN model having a root mean square and absolute error approximately double that of the DT model. The superior performance of DT and RF models can be attributed to their ability to handle non-linear relationships and interactions among features, which are common in accident rate prediction. DT is particularly effective in capturing hierarchical and rule-based patterns in the data, while RF further enhances this by reducing overfitting through ensemble learning. In contrast, ANN and SVM models require larger datasets for effective training and often struggle with overfitting and optimization challenges when applied to small datasets, as in this study. To evaluate the relative importance of different attributes, we analyzed their weights. Figure 11b presents the results for the two best-performing models, DT and RF. Both models assign high relative importance to rut depth values, weather conditions, and study location. Notably, the relative weight of rut depth was considerably higher for DT (53%) compared to RF (34%). The results demonstrate the potential of employing machine learning algorithms to develop prediction models for accident rates based on rut depth under varying conditions. However, the models were built using a limited and unbalanced dataset, which may raise concerns about their reliability. To enhance reliability, it is essential to create comprehensive and sizable datasets for developing more robust prediction models.