## 3 Results and Discussion
### Safety Predictive Toolkit Model  
The toolkit constructs a predictive model to predict the safety of each identified age group. The features are detailed in Table 3. Model characteristics (Figure 2) depict the ROC curve, Gain and Lift Charts as output. The obtained lift and gain values are explicitly defined in Table 4. Table 5 displays the AUROC scores to verify the model's believability by measuring its distinguishable capability to forecast safety correctly. There were 3225 crashes reported in the study area, divided into 1420, 312, 481, 422, 307, 167, 76, and 40 for the 0-16, 17-20, 21-29, 30-39, 40-49, 50-59, 60-69, and \(>\)70 age groups, respectively. Figure 1: The overview of the (**a**) Cyclist flow cameras and (**b**) Motor vehicle cameras in the study area. Figure 2: (**a**) ROC curve, (**b**) Gain chart, and (**c**) Lift chart for the constructed predictive deep learning-based model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & Layer Units & 172 \\ \hline \multirow{4}{*}{**Hidden Layer(s)**} & Number & 2 \\ \cline{2-3}  & Layer units & 350 \\ \cline{2-3}  & \(\text{A}_{\text{f}}\) & Hyperbolic tangent \\ \cline{2-3}  & \(\text{E}_{\text{f}}\) & Cross-entropy \\ \cline{2-3}  & \(\text{C}_{\text{ee}}\) & 252.7 \\ \hline \multirow{4}{*}{**Output Layer**} & Layer units & 7 \\ \cline{2-3}  & \(\text{A}_{\text{f}}\) & SoftMax \\ \cline{2-3}  & \(\text{E}_{\text{r}}\) & Cross-entropy \\ \cline{2-3}  & \(\text{C}_{\text{ee}}\) & 1392.2 \\ \cline{2-3}  & \(\text{A}_{\text{f}}\) = Activation function, \(\text{E}_{\text{r}}\) = Error function, \(\text{C}_{\text{ee}}\) = Cross entropy error. & \\ \end{tabular}
\end{table} 
Table 3: The model features of the predictive model. The AUROC values obtained for the over 65 (91 per cent), 55-64 (95 per cent), 45-54 (95 per cent), 35-44 (95 per cent), 25-34 (95 per cent), 25-34 (95 per cent), 17-24 (95 per cent), and under 17 (97 per cent) age groups indicate a high distinguishable capability between the safest and non-safe scenarios, for each of the output variables. The overall model accuracy was 95%, with the same median accuracy and standard deviation of 2%. The results show that the predictive safety toolkit can be used to forecast and model safety in real time with high accuracy. Only a few models in the current literature can distinguish between safe and unsafe scenarios with such reasonable accuracy and efficiency. Most collision prediction models have a forecast success rate of less than 50%, which is well documented in the literature [16]. The model comparison with the accuracy available in the literature is shown in Figure 3. According to [PERSON] et al. (2013), traditional models are designed for the assignment of motorized modes of transportation and are insufficient for the needs of cyclists because they are unable to quantify the influence of the bicycle safety performance function [17]. According to a survey on safety models [15], for these reasons, more than 70% of European road authorities seldom or never use the collision prediction model in their decision making. The developed model outperforms the current standard road safety models in the literature. This is due to two primary elements: (a) the deep learning neural network's capacity to simulate the non-linear and complicated interaction between input and output variables, and (b) the selection of suitable compounding components that represent an actual danger to the cyclist rather than perceived or motorist bias variables. Hence, the model created by the safety toolkit is a significant contribution to the literature. The results show that it is possible to predict safety in real-time, contrary to the established convention. Therefore, based on the results, it is recommended that such models are used to model safety for the effective and efficient design of transportation networks and bespoke infrastructure. It has an extra impetus for a vulnerable road user that is subjected to a many-fold higher risk than a motorist (see [18; 19; 20]).