## 2 Related Work
### Which Models Should Be Used to Predict Mode Choices?
Algorithms such as the k-nearest neighbor algorithm and the Bayesian classifier are characterized by similar shortcomings, which are contributed by prediction results without prior learning and independence assumptions, which prevent these models from having the enviable power of classifying more complex data. The aforementioned limitations can be replaced by the application of artificial neural network models that provide the possibility of modeling complex nonlinear relationships thanks to layers with nonlinear activation functions, giving better predictive results. This is evidenced by recent studies that show that artificial neural networks are an effective alternative for predicting travel modes (see Table 1). Although the focus of the analyzed studies was on the choice of the mode of travel, what is common to the study conducted in this paper is the analysis of the factors that influence their case choice, and in the present case, the acceptability of e-bikes. Accordingly, finding a more efficient method is very challenging because, in addition to the choice of travel method, the willingness to accept a new mode of transportation is a very complex process. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Classification** & **Data Sources** & **Transport Modes** & **Model Prediction** & **Important Variables** \\ \hline \hline \multirow{4}{*}{[PERSON] et al. (2018)} & \multirow{4}{*}{DT, NN, MNL} & \multirow{4}{*}{\(n=34\),680 observations} & Single-occupancy vehicle, & DT = 76.8\%; NN = 78.2\%; MNL = 72.9\% & Socio-demographic and economic attributes, number of vehicles in the household and license ownership, service level attributes, and travel time and costs. \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \multirow{4}{*}{[PERSON] et al. (2016)} & \multirow{4}{*}{MLP, RBF, Bayes, DT, ENN, KNN, MNL, SVM} & \multirow{4}{*}{\(n=3673\) households} & Private car, bus, train, cycling, walking & DT = 78\%; KNN = 83\%; KNN = 86\%; MNL = 62\%; SVM = 79\%; & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \multirow{4}{*}{[PERSON] et al. (2019)} & \multirow{4}{*}{MLP, RBF, MLN, SVM} & \multirow{4}{*}{\(n=3670\) observations} & Car, public transport, cycling, walking & MLP = 81.1\%; RBF = 79.2\%; MNL = 67.7\%; SVM = 64.7\% & Costs, local and demographic variables, availability of transportation, and geographic information. \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the selected literature on the application of different models for predicting the choice of travel mode. In this regard, this paper will try to use two different artificial neural network models (multilayer perceptron and radial basis function) to reveal the variables that have the greatest influence on the choice of electric bicycles during trips and indicate their predictive power.