## 4 Result and Discussion  
### Findings on Established Factors in Roadside Collision Research  
Figures 1 and 2 show the distribution of cleaned NCDB of Canada versus different factors available in the dataset. Figure 1, provides a visual comparison of distributions among all cleaned datasets and collisions between two vehicles when one hits a parked motor vehicle. The pattern of distribution of data is almost similar in both the whole and the subset dataset. Figure 1a,b show an increase in the number of collisions-or risk of collision-in high travel seasons, such as the summer and holidays. Similarly, Fridays and Saturdays have higher rates and probability of collisions (Figure 1c,d). This is highlighted for the collisions with a parked motor vehicle. The peak collision hour in the morning is around 8:00 am and in the afternoon is between 15:00 to 17:00 (Figure 1e,f). These findings appear to explain some of the seasonal and time-specific effects that were observed in the previous studies such as those identified by [15; 24]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Classifier** & **Test Score** & **Balanced Accuracy** & **Precision** & **Recall** & **f1** & **Custom Error** \\ \hline
1 & AdaBoost (Adaptive Boosting) & 0.64 & 0.64 & 0.60 & 0.75 & 0.67 & 0.36 \\ \hline
2 & RF (Random Forest) & 0.62 & 0.62 & 0.61 & 0.60 & 0.61 & 0.38 \\ \hline
3 & LDA (Linear Discriminant Analysis) & 0.62 & 0.62 & 0.59 & 0.73 & 0.65 & 0.38 \\ \hline
4 & Extra Trees & 0.62 & 0.62 & 0.60 & 0.62 & 0.61 & 0.38 \\ \hline
4 & MLP (Multi-Layer Perceptron) & 0.62 & 0.62 & 0.60 & 0.66 & 0.63 & 0.38 \\ \hline
6 & GaussianNB (Gaussian Naive Bayes) & 0.57 & 0.56 & 0.56 & 0.47 & 0.52 & 0.43 \\ \hline
7 & DT (Decision Tree) & 0.56 & 0.56 & 0.54 & 0.63 & 0.58 & 0.44 \\ \hline
8 &
\begin{tabular}{c} LinearSVC (Linear Support \\ Vector Classification) \\ \end{tabular} & 0.49 & 0.50 & 0.49 & 1.00 & 0.65 & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Auto-Sklearn metric results. Although the traffic hours vary widely from location to location, it can be observed that the number of collisions during rush hours is higher. The higher rate of collisions with a parked vehicle at night time compared to other types of collisions may show the importance of visibility in this type of collision. Figure 1(a) shows the contribution of the age of vehicles in collisions. The normalized standard deviation of the number of collisions for all vehicles is about 1.0. However, the same metric for those vehicles under 15 years old is 0.14. This illustrates that there is no significant difference between the number of collisions when vehicles are under 15 years old. Comparing this finding with the total annual number of new motor vehicles sold in Canada [37] shows that the rate of accidents involving new vehicles is lower than the old ones. This may reflect the improved safety standards and regulations, as well as increased usage of advanced collision avoidance systems (i.e., automatic emergency braking, traction, and electronic stability controls, etc.). The combination of safety measures and guidelines [20] with new detection technology appears to be effective in reducing accidents and may shed light on the extent to which commonly identified factors or predictors of collisions in the previous research remains relevant. Circumstantial conditions such as lightning and road infrastructure [8], vehicle design, and safety specifics [7,10] are examples of predictors that deserve further analysis. Demographic factors, such as gender and driver's age, also appear to be relevant, as predicted by the literature [8,16]. Men, for instance, are recorded in collisions about 1.2 times more than women (Figure 1(b)). This may be the result of risky driving practices such as not using safety belts, driving while impaired by alcohol, and speeding as specified in other studies [38]. Most of the people who are vulnerable to accidents are between 16 to 26 years old while two-thirds of them are drivers (Figure 1(c),e).