## 3 Results
### Multiple Correspondence Analysis
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & **Categories** & **Coding** \\ \hline \% Users recovered (R) & \(\leq\)70\% & R1 \\  & 70\% + x \(\leq\) 80\% & R2 \\  & 80\% + x \(\leq\) 90\% & R3 \\  & -90\% & R4 \\ \% Vehicle occupancy 2022 (O) & \(\leq\)25\% & O1 \\  & 25\% + x \(\leq\) 50\% & O2 \\  & 50\% + x \(\leq\) 75\% & O3 \\  & \(>\)75\% & O4 \\ Frequency (min) (F) & \(\leq\)20 & F1 \\  & 20 \(\times\) x \(\leq\) 25 & F2 \\  & 25 \(\times\) x \(\leq\) 30 & F3 \\  & \(>\)30 & F4 \\ Length of line (km) (L) & \(\leq\)10 & L1 \\  & 10 \(<\) x \(\leq\) 20 & L2 \\  & 20 \(<\) x \(\leq\) 30 & L3 \\  & \(>\)30 & L4 \\ Suburban (S) & YES & SY \\  & NO & SN \\  & TYES & IY \\  & NO & IN \\ Nodality (N) & Hospital & NY1 \\  & Hospital/University & NY2 \\  & Mall & NY3 \\  & University & NY4 \\  & NO & NN \\ Circular (C) & YES & CY \\  & NO & CN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Qualitative and quantitative variable coding. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Dimension**} & \multirow{2}{*}{**Cronbach’s Alpha**} & \multicolumn{2}{c}{**Variance Accounted for**} \\ \cline{3-4}  & & **Total (Singular Value)** & **Inertia \% Proportion of Inertia** \\ \hline
1 & 0.839 & 3.756 & 0.469 & 46.947 \\
2 & 0.790 & 3.243 & 0.405 & 40.535 \\ Total & & 6.999 & 0.875 & \\ Mean & 0.816 & 3.499 & 0.437 & 43.741 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MCA output. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Dimension**} & \multirow{2}{*}{**Average**} \\ \cline{2-3}  & & **1** & **2** \\ \hline R & **0.734** & **0.634** & 0.684 \\ O & **0.656** & 0.389 & 0.522 \\ F & 0.517 & **0.536** & 0.527 \\ L & 0.492 & 0.475 & 0.484 \\ S & **0.690** & 0.021 & 0.355 \\ I & 0.076 & 0.312 & 0.194 \\ N & 0.580 & **0.488** & 0.534 \\ C & 0.011 & 0.388 & 0.200 \\ \hline Total & 3.756 & 3.243 & 3.499 \\ \hline \multicolumn{3}{l}{\% Proportion of inertia} & 46.947 & 40.535 & 43.741 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MCA measures of discrimination. With a global inertia of 87.4% for the two dimensions, Dimension 1 discriminates the variables from the percentage of users recovered, the level of occupancy of the vehicles, and the urban or suburban route of the buses. Dimension 2 does so from the percentage of users recovered, frequency, and nodality. The fact that the level of occupancy discriminates in the two dimensions can make the interpretation of the results more complicated. Figure 4 illustrates the perceptual plot of both dimensions. As can be seen in Figure 4, the dispersion of the variables does not highlight any robust correlation between the variables as a result of the MCA. By analyzing Cluster 1, and in the specific case of the percentage of users recovered in 2022 compared to 2019 (\(R\)), only a weak correlation is found with variables such as frequency (\(F\)) and average occupancy of vehicles (O). In fact, there is a relationship between the maximum recovery of users and the lowest occupancy, due to the influence of lines L15 and L19, which are the ones with the lowest average occupancy (19% and 25%, respectively) and the highest percentage of users recovered compared to the pre-pandemic situation (Table 2). The MCA also suggests a good behavior of the lines serving the university (\(NY4\)). These results confirm the reliability of the statistical model.