## Materials and Methods
### Phase-II: Application of ANNs Model for Risk Prediction and Evaluation  
In the second phase, the dependent variable is the risk value generated by the DEA model, was transformed by applying natural log to have data normalized. For independent variables, speed could be controlled by controlling the speed limit; flow was directly related to the number of vehicles, and could be controlled by controlling access; horizontal curve could be removed or altered as per infrastructural changes, and the same was the case for vertical curve as a geometric design feature. So for the application of ANN, data was distributed on the basis of a K-Fold mechanism with five folds (i.e., distribution is as 53 segments-DMUs for Training and 14 segments-DMUs for validation). ANNs, unlike other modeling platforms, requires some form of model validation to aid in the model-building process and to help prevent overfitting of the model. The basic idea behind validation (or cross-validation) is to hold a subset of the data out of the model-building process. This process forms two partitions of the data, a training set and a validation set (note that a third set, or test set, can also be used). The model is built using the training set, while the k-fold validation set is then used to assess how well the model performs, and to aid in model selection. The most mainstream decision for the quantity of concealed layers is used. A solitary concealed layer is typically adequate to catch even extremely complex connections between the indicators. The quantity of links in the shrouded layers likewise decides the level of multifaceted nature of the connection between the indicators that the system catches [57]. From one viewpoint, utilizing an excessive couple of links is not adequate to catch complex connections (e.g., review the unique instances of a straight relationship in direct and calculated relapse, in the extraordinary instance of zero links or no shrouded layer). Then again, an excessive number of links may prompt overfitting. A dependable guideline is, to begin with (number of indicators) links and reduce or increment gradually while checking for overfitting. Another approach is to start with the default neural model, with one layer and four nodes, and then run a much more complex model with two layers and several nodes, and different activation functions. If the fit statistics do not improve substantially with a more complex model, then a simpler model may suffice [57]. We applied a simpler model to check the performance of the ANNs model in our case of risk evaluation, as shown in Figure 3. After running the model as shown in Figure 3, we displayed the model structure. We saw input variables mapping to each of the activation functions in the hidden layer, and nodes in the hidden layer mapping to the output layer. The background mechanism in each of the nodes in the hidden layer designated that the Gaussian activation function was used. Model results for both the training and validation sets are shown in Table 4. The response variable (risk) for this model was continuous. Like other techniques, it was necessary to follow the validation mechanism. With the validation mechanism and separation of the data into two sets, unbiased results were provided. Figure 3: Risk Prediction GIS Map based on the ANNs Model. In this study, as discussed earlier, original data were distributed into two parts. Out of 67 segments, 53 as the major data set were used for the training of the ANNs setup, while the remaining 14 were used for validation after model building. The training set was the part that estimated model parameters. The validation set was the part that assessed or validated the predictive ability of the model. In addition, the most critical validation was applied in this study. Specifically, the K-Fold technique was adopted, which divided the original data into K subsets. In turn, each of the K sets is used to validate the model fit on to the rest of the data, fitting a total of K models.