## 6 Discussion
However, the results of the model trained with the hyperbolic tangent function showed smaller sum-of-squares error values in both training and testing (28.828 and 13.217, respectively). This indicated that the MLP model, in which the data were split for training, testing, and retention with 60%-20%-20%, respectively, with the hyperbolic tangent used as the activation function in the hidden layer and the identity function in the output layer, gave a better validation result and proved the high ability of the model to predict the user's attitude through two possible responses. Moreover, by establishing an acceptable architecture with an appropriate number of hidden layers and neurons, it was important to accurately identify factors for the acceptability of e-bike use. This is also indicated by the parameters of the respondents' correctly classified answers, which show the significant predictive power of the MLP model (accuracy 88.7%) compared to the RBF model (accuracy 80.5%). Also, such results are confirmed by the parameters used, such as accuracy, precision, FP rate, recall, F1, MCC, and values under the ROC curve, as well as gain and lift graphs, which are constructed on combined training and testing examples and produce the best combination of sensitivity and specifics. As a model with better predictive ability, MLP also indicates the importance of independent variables on the basis of which the acceptability of using sustainable e-bikes depends primarily on the average mileage traveled when commuting (NI = 100%). This is confirmed by the results of the chi-square test of independence (\(\chi^{2}\) = 87.399; \(p<0.001\)). In support of this, respondents who on average travel from 2.5 to 5 km and from 5 km to 8 km in one direction when commuting (27.2% and 25.7%, respectively) would accept to the greatest extent a move to using e-bikes. This also indicates that the distance during trips can significantly affect the attitude of the examinee, so the mileage traveled during trips with other purposes for the MLP model (NI = 83.2%) and the chi-square test of independence was significant (\(\chi^{2}\) = 35.370; \(p<0.001\)). Such results are encouraging considering that on shorter distances, instead of motorized transport, respondents opt for sustainable e-bikes and thus influence the reduction in congestion and environmental pollution. Looking at the socio-demographic and economic characteristics of the respondents, age (NI = 72.6%) and level of education (NI = 71.7%) stand out as significant variables from the perspective of the MLP model. In support of this, respondents aged 18 to 25 (51.6%) and having completed basic studies at university (38.9%) are most willing to use e-bikes. These results are in line with the findings of other studies that point out that e-bikes are increasingly attracting a younger, highly educated population [13; 45]. On the other hand, employment (NI = 45.5%) and gender (NI = 32.6%) were not significantly significant for making the decision to use e-bikes, which is also confirmed by the study which indicates that instead of gender and differences according to employment status, the health status of the user had a greater importance for the use of e-bikes [51]. On the other hand, the frequency of using motorcycles when commuting (NI = 97%), and then also for other purposes (NI = 85.3%) also has great significance in accepting e-bikes. Namely, such results were expected, considering that even 96.1% and 94.0% of respondents, respectively, who do not use this mode of transportation for any purpose said that they would accept the use of sustainable e-bikes. Also, observing the trip characteristics, the frequency of use of other modes of transportation when commuting can be singled out as significant (NI = 71.0%), which is also confirmed by the chi-square test of independence (\(\chi^{2}\) = 13.394; \(p=0.010\)). Such results are very favorable, bearing in mind that respondents who never use this mode of transportation (65.6%) are the most willing to use sustainable e-bikes.