## 3 Materials and Methods
### Development of a Multilayer Perceptron Model (MLP)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Classification Algorithm \(\star\)** & **Data Sources** & **Transport Modes** & \begin{tabular}{c} **Model Prediction** \\ **Accuracy** \\ \end{tabular} & **Important Variables** \\ \hline \multirow{3}{*}{[PERSON] et al. [67]} & BPN, RBE, PNN, & \(n=10,500\) & \multirow{3}{*}{Walk, bike, transit} & \begin{tabular}{c} BPN = 79.4\%; \\ RBF = 78.4\%; \\ PNN = 82.9\%; \\ CPN = 83.3\%; \\ MNL = 70.5\% \\ \end{tabular} & \begin{tabular}{c} The cost of the car and transit cost \\ were the sensitive variables, notably \\ for MNL and BPN models. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [69]} & ELM, SVM, MLP & \(n=2747\) & Passenger car, walk & \begin{tabular}{c} ELM = 97.30\%; \\ SVM = 89.53\%; \\ MLP = 98.99\% \\ \end{tabular} & \begin{tabular}{c} Family income, travel time, and \\ parents’ education level. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [70]} & BLR, MLP & \(n=503\) & \begin{tabular}{c} Electric \\ observations \\ \end{tabular} & \begin{tabular}{c} BLR = 84.7\%; \\ MLP = 85\% \\ \end{tabular} & \begin{tabular}{c} Attitudes about pollution, use of \\ bicycles and motorcycles, mileage, \\ and income. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [71]} & \multirow{3}{*}{BPN, TAN} & \(n=2000\) & Walk, bike, private & \
\begin{tabular}{c} BPN = 72.2\%; \\ ran = 75.4\% \\ \end{tabular} &
\begin{tabular}{c} Traveler characteristic, household \\ characteristic, and trip \\ characteristic. \\ \end{tabular} \\ \hline \multirow{6}{*}{[PERSON] et al. [71]} & \multicolumn{3}{c}{\(\star\) Classification Algorithm: NN—neural network; MLP—multilayer perceptron; RBF—radial basis function; Bayes—Bayes classifier; DT—decision tree; ENN—evidential neural network; KNN—k-nearest neighbor algorithm; MNL—multinomial logit; SVM—support vector machine; ELM—extreme learning machine; BPN—backpropagation neural network; PNN—probabilistic neural network; CPN—clustered probabilistic neural network; BLR—binary logistic regression; MLR—Multiple Linear Regression Model; TAN—Tree Augmented Naive Bayes.} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The MLP is trained to minimize the errors between the desired target values and the values calculated from the model. In the learning procedure, data sets of input and desired target template pairs are sequentially presented to the network. The net input to the neuron \(j\) of the hidden layer for the pattern \(p\) (\(NET_{p,j}\)) is calculated as the sum of each output of the input layer (\(x_{p,i}\); input value) multiplied by the weight (\(v_{p,i}\)). The activation function is applied to calculate the output of neuron \(j\) of the hidden layer (\(z_{p,j}\)) and the output of neuron \(k\) of the output layer (\(o_{p,k}\)) [75] in the manner shown by Equation (1):  
\[f(NET)=\frac{1}{1+\exp(-\lambda NET)} \tag{1}\]  
where \(\lambda\) is the coefficient of the activation function, and \(NET\) is expressed either as \(z_{p,j}\) or \(o_{p,k}\)[76] in the manner shown by Equations (2) and (3):  
\[z_{p,j}=f\left(\sum_{i}x_{p,i}v_{p,i}\right) \tag{2}\]  
\[o_{p,k}=f\left(\sum_{j}z_{p,i}w_{p,kj}\right) \tag{3}\]  
where \(v_{p,i}\) and \(w_{p,kj}\) are the connection weights between neuron \(i\) in the input layer and neuron \(j\) in the hidden layer, and those between neuron \(j\) of the hidden layer and neuron \(k\) of the output layer for pattern \(p\), respectively. The weights are initialized as small random numbers. Different activation functions can be used, such as linear functions, threshold functions, and sigmoid functions. The most commonly used function is the unipole (or logistic) sigmoid due to its nonlinearity [77]. The learning algorithm modifies the weights (\(v_{p,i}\) and \(w_{p,kj}\)) to minimize the error.