## 5 Research Results
### Prediction Results of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) Models  
The maximum time for training both network models was set to a maximum of \(15\) min, while the time for testing was automatically determined by the software. Manually created architectures of the MLP network with nine neurons in the first and seven neurons in the second hidden layer (Figure 3a) and of the RBF network with thirty neurons in only one hidden layer (Figure 3b) gave the best results. Different activation functions were used for different layers of the MLP and RBF networks, i.e., in the hidden layer, there are the hyperbolic tangent and Softmax functions, respectively, and in the output layer the identity function for both network models. After determining its basic characteristics, the network was trained and then tested. The time required to train both networks was significantly different, indicating that the  
Figure 3: Schematic representation of the structure of artificial neural networks (ANNs): (**a**) multilayer perceptron model; (**b**) radial basis function model. MLP network required less training time (0.20 s) compared to the RBF network (3.63 s). The Sum of Squares error function values indicate that the MLP network both during training and testing has lower error values (28.828 and 13.217, respectively) compared to the RBF network (33.999 and 15.439, respectively), which indicates that the MLP model has smaller deviations from the predicted values and during training and testing compared to the real thing. Considering the large number of attributes and the sample size, the MLP and RBF network models in the training phase give very good results in predicting users who would agree to use e-bikes to reduce congestion and environmental pollution (96.9% and 94.8%, respectively). On the other hand, both networks have poorer accuracy in predicting responses that have a negative attitude compared to users who would use e-bikes. Namely, with an accuracy of 75.6%, the MLP model has slightly better accuracy compared to the RBF model (65.9%), whose accuracy is acceptable. The confusion matrix shown in Table 5 indicates the final prediction results observed after testing the data. The final results indicate that both models can accurately classify users into two groups, taking into account all the attributes that contributed to the final answer, similar to the results during training. In this regard, the MLP and RBF models predicted a significantly worse number of users who are not willing to use e-bikes (62.5% and 47.8%, respectively), which, in addition to the small sample, may indicate a worse dependence on the attributes (independent variables) and the final answer, which additionally had a negative impact on both training and data testing. On the other hand, the classification accuracy for those users who would use e-bikes is incomparably better with the models (95.0% and 88.9%), indicating the good training of both models, which contributed to the recognition of such responses by users in a larger number. Finally, the comprehensive classification results during testing indicate a high predictive power of the MLP model that performs significantly better than RBF (88.7% and 80.5%, respectively). In order to further determine the prediction accuracy performance of the MLP and RBF models during data testing, model validation using the accuracy parameter was conducted. Analogous to the prediction accuracy of the entire data set, the accuracy parameter indicates significantly good results for both models (>0.8) (see Table 6). This is indicated by the rate of false-positive events, which represents the ratio between the number of negative events incorrectly classified as positive and the total number of real negative events (regardless of classification). On the other hand, the precision of the RBF model is not so significant when classifying the users who have a negative attitude toward the use of e-bikes (0.524) compared to the MLP model (0.750), which also has a better overall precision. Then, the