## 3 Results and Discussion
### Predictive Model  
The findings of the deep learning prediction model that predicts the riskiest age and gender group based upon infrastructure input factors are reported in this section. Two deep learning models were built. Their accuracy is measured by comparing their distinguishable power between the riskiest and non-riskiest age and gender groups using AUROC, as shown in Table 4. The output predicted versus the observed for the testing datasets is presented in Figure 3, and the ROC curves for each output variable in Figure 4. It can be inferred from the ROC curves and the AUROC values that both the models attained a high level of accuracy. Each model can have up to seven different output values. As a result,with perfect precision, the highest value of AUROC can be seven. The male and female AUROC values obtained were 6.19 and 6.64, respectively. This suggests that the prediction and distinguishable power is high and consistent. As the physical and cognitive abilities of riders of different ages and gender groups vary, there are safety implications for each rider sub-group. Hence, it makes it possible to estimate the most dangerous age and gender groups based on the individual input data.  
The literature widely reports that current safety models cannot be directly utilised to simulate bike infrastructure, due to their inability to appropriately represent safety. In one comprehensive work to evaluate the efficacy of such a model, the Finnish crash model was gauged for its efficacy to model cyclist safety [30]. The study revealed an inaccuracy of around 65%. Similarly, the evaluation of the primary transportation simulation software, including PTV VISSIM, AIMSUM, TEXAS, and PARAMICS, exhibited an incapacity to mimic riders adequately and efficiently [31]. These studies concluded that cyclist interaction cannot be simulated using such packages, developed on the premise of motorised modes of transport. Hence, the accuracy achieved through the developed learning system is many times higher than the custom models in the literature. To further demonstrate the benefit of using the complex methodology for prediction, lift and gain charts are presented in Figures 3-6. These charts compare the efficacy of the output compared with the traditional probability-based statistical model.  
Figure 4: The ROC curve for each output variable of: (**a**) male, (**b**) female.  
Figure 5: The gain chart for each output variable of: (**a**) male, (**b**) female.  
Figure 6: The lift charts for each output variable of: (**a**) male, (**b**) female.  
For all the variable cases, the ROC curve line is closer to the upper left corner, farther away from the 45\({}^{\circ}\) basslines, which depicts significantly high prediction capability, evident from the AUC values. The gain is a measure of the effectiveness of the constructed model calculated as the percentage of the correct predictions obtained within the model versus the accurate predictions obtained without the model, i.e., baseline. A significant higher gain is obtained for all the output variables (an average of 10%, 80%), i.e., if we sort the wrong prediction by their pseudo probabilities, the top 10% of the dataset will have all 80% cases of improper predictions. Similarly, from the gain chart, the average gain value for the output, at 10% data is eight, i.e., the average accuracy of the models is eight times higher in comparison to the base case at this point.