## 6 Discussion  
The use of nonlinearity in the research of attitudes toward the use of e-bikes in Belgrade helps to answer a complex question related to the factors that influence the acceptability of the use of this sustainable form of trips. Although the choice of travel mode, i.e., the change in transport behavior, has been widely investigated in the scientific literature [58; 59; 60; 61; 62], this study sought to predict the extent to which the awareness of sustainable transport is represented among the residents of Belgrade, which has recently been increasingly traffic-congested and environmentally polluted. In particular, the main findings of this study indicate that in addition to daily habits in terms of trip characteristics, awareness of the level of environmental pollution significantly affects the respondents' final attitude toward the use of e-bikes. Bearing in mind this connection, it is possible to predict that by changing habits when moving, and with the possibility of a less-polluted environment, the increased use of e-bikes could be significantly influenced. By selecting artificial neural networks as significant predictors [63; 64; 65] based on previous studies of transport behavior, in this paper, MLP and RBF models are trained using backpropagation algorithms to obtain the main factors that influence the attitude toward the use of e-bikes. In order to develop models based on the performance of neural net \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Importance** & **Normalized Importance** \\ \hline Mileage when commuting & 0.064 & 100.0\% \\ \hline Frequency of motorcycle for commuting trips & 0.062 & 97.0\% \\ \hline Frequency of motorcycles for trips with other purposes & 0.054 & 85.3\% \\ \hline Mileage for trips with other purposes & 0.053 & 83.2\% \\ \hline Pollution of Belgrade by pollutant emissions from road vehicles & 0.047 & 73.8\% \\ \hline Age & 0.046 & 72.6\% \\ \hline Education & 0.046 & 71.7\% \\ \hline Frequency of other modes of transport for commuting trips & 0.045 & 71.0\% \\ \hline Unfavorable type of terrain & 0.045 & 70.6\% \\ \hline (No) safety during the trip for e-bicycle users & 0.044 & 69.5\% \\ \hline Frequency of bicycle for commuting trips & 0.042 & 65.1\% \\ \hline Price of e-bicycle & 0.039 & 60.7\% \\ \hline Monthly personal income & 0.038 & 58.8\% \\ \hline Frequency of walking for trips with other purposes & 0.037 & 57.7\% \\ \hline Frequency of bicycle for trips for other purposes & 0.034 & 53.5\% \\ \hline Pollution of Belgrade by noise from road vehicles & 0.034 & 53.3\% \\ \hline Frequency of use of other transport modes for trips with other purposes & 0.033 & 51.9\% \\ \hline Frequency of public transport for commuting trips & 0.033 & 51.2\% \\ \hline Frequency of passenger car use for trips with other purposes & 0.031 & 48.8\% \\ \hline Employment & 0.029 & 45.5\% \\ \hline Non-existent infrastructure & 0.028 & 44.2\% \\ \hline Frequency of passenger car use for commuting trips & 0.027 & 42.6\% \\ \hline Frequency of public transport for trips with other purposes & 0.026 & 41.2\% \\ \hline No existent parking spaces for e-bicycles & 0.021 & 33.1\% \\ \hline Gender & 0.021 & 32.6\% \\ \hline Frequency of walking for commuting trips & 0.021 & 32.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Independent variables’ importance in the MLP model. works, it was necessary to create and develop numerous ANN models in the MLP and RBF structures. This, in addition to determining the ideal number of neurons, hidden layers, and transfer functions, also affects the reduced complexity of the model, and therefore leads to less overloading [88; 89]. In this regard, in addition to the 27 input neurons and 2 output neurons that characterize both network models, the best performance was achieved by the MLP model with 9 neurons in the first and 7 neurons in the second hidden layer and the RBF model with 30 neurons in only one hidden layer.