## 7 Conclusions  
With the aim of finding a solution for traffic congestion and environmental pollution in Belgrade, this study dealt with the understanding of the factors that influence the change in travel habits and the prediction of users' attitudes regarding the use of e-bikes. The key findings of this study show that the highest accuracy is achieved using a multilayer perceptron (MLP) neural network with a 6-2-2 partition, which has 26 neurons in the input layer, 9 and 7 neurons in the two hidden layers, respectively, the hyperbolic tangent used as the activation function in the hidden layer, and the identity function in the output layer. The better ANN model was identified with a smaller sum-of-squares error value of 13.217, a correct classification rate of 88.7%, and an area under the ROC curve for each category with predicted pseudo-probability (I would accept = 0.927 and I wouldn't accept = 0.927). In addition, based on the results, it seemed that the MNL model was the most acceptable for both stated attitudes. Moreover, according to the neural network analysis, the most powerful predictors for making the final decision to use e-bikes were the mileage traveled when commuting (NI = 100.0%), the frequency of using motorcycles, and when commuting (NI = 97.0%) and for other purposes (NI = 85.3%). Following these were the average mileage for trips for other purposes (NI = 83.2%), the opinion of the respondents on the pollution of Belgrade from the aspect of pollutant emissions from road vehicles (NI = 73.8%), and the age of the inhabitants (NI = 72.6%).  
The obtained results can play a key role when adopting strategies to reduce traffic congestion and environmental pollution in Belgrade. Namely, in addition to the fact that 69.8% of respondents are willing to use e-bikes and that the MLP model has a high ability to predict attitudes, understanding the influencing factors in this study can be of great importance to decision-makers. Future studies on the acceptability of new concepts of ways of transportation could deal with the analysis of the acceptability of the use of shared e-bike systems as an incentive to change habits during trips. Although, despite the small sample size, the MLP model gave very good prediction results, it is suggested that future studies be based on increasing the sample, as well as conducting field research in order to obtain more precise answers. The role of an interviewer would greatly help to resolve doubts during the survey and give more accurate answers, which, in addition to influencing a better sample, would have significant advantages in the formation, and later the training and testing, of the model. In addition, future studies could be extended to other classifiers, such as support vector machines, and be used for testing other kernels different from Gaussians. Also, thoughts on further research can go in the direction of combining artificial neural network models and traditional transport system models in order to define a new approach to promoting sustainable modes of transport [92].  
Conceptualization, J.K., S.K. and D.G.; methodology, J.K.; validation, S.K. and D.G.; writing--original draft preparation, J.K.; writing--review and editing, S.K. and D.G.; visualization, J.K., S.K. and D.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.