# Modeling Public Transportation Use Among Short-Term Rental Guests in Madrid
## 3 Results
### Ordered Logistic Regression Marginal Effects
The number of POIs visited is strongly associated with greater public transport use. A 1% increase in the visited attraction decreases the probability of low use (\(-2.9\%,p=0.00\)) and medium use (\(-0.9\%,p=0.00\)) and increases the probability of high use (+3.8%, \(p=0.00\)), indicating that more active tourists are also heavier users of public transport. The importance given to public transport when choosing accommodation is a strong predictor. Tourists who rated this factor as high show a significantly lower likelihood of low use (\(-23.3\%,p=0.00\)) and a higher likelihood of high use (+28.3%, \(p=0.00\)) compared to those who considered it unimportant. Similarly, those who considered it of medium importance are less likely to have low use of public transport (\(-15.9\%,p=0.00\)) and a higher likelihood of high use (+23.1%, \(p=0.00\)). Notably, these marginal effects are higher in absolute value for those who reported high importance given to public transport when choosing accommodation than for those who reported medium importance. Accommodation location also plays a significant role. Staying outside the city center increases the probability of high public transport use: tourists in the central almond or the periphery are, respectively, 15.4% (\(p=0.02\)) and 27.4% (\(p=0.00\)) more likely to be in the high-use category compared to those in the city center. Conversely, the likelihood of falling into the low-use group is 9.8% lower (\(p<0.00\)) for individuals staying in the central almond and 15.1% lower (\(p<0.00\)) for those staying in the periphery. Notably, the absolute values of these marginal effects are greater for tourists staying in the periphery. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Low Use**} & \multicolumn{2}{c}{**Medium Use**} & \multicolumn{2}{c}{**High Use**} \\ \cline{2-7}  & \(\mathbf{dyldx}\) & \(\mathbf{p}\) & \(\mathbf{dyldx}\) & \(\mathbf{p}\) & \(\mathbf{dyldx}\) & \(\mathbf{p}\) \\ \hline
**First time in the city: Yes** & **0.07020** & 0.02 & **0.01870** & 0.04 & \(\mathbf{-0.08890}\) & 0.02 \\
**Group type: Solo** & \(-0.04420\) & 0.48 & \(-0.01910\) & 0.60 & 0.06330 & 0.52 \\
**Group type: Couple** & \(-0.00377\) & 0.90 & \(-0.00113\) & 0.91 & 0.00490 & 0.90 \\
**Origin: Europe** & **0.13200** & 0.01 & **0.02850** & 0.02 & \(\mathbf{-0.16100}\) & 0.01 \\
**Origin: America** & 0.07630 & 0.17 & **0.01530** & 0.06 & \(-0.09160\) & 0.14 \\
**Origin: Rest** & **0.20900** & 0.05 & \(-0.02320\) & 0.58 & \(\mathbf{-0.18600}\) & 0.00 \\
**Access to city: Land-based** & 0.05500 & 0.27 & 0.01110 & 0.11 & \(-0.06610\) & 0.23 \\
**Access to city: Private car** & 0.12500 & 0.14 & 0.00315 & 0.86 & \(\mathbf{-0.12900}\) & 0.07 \\
**Number of nights** & \(-0.00838\) & 0.17 & \(-0.00247\) & 0.21 & 0.01090 & 0.17 \\
**Number of visited tourists attractions** & \(\mathbf{-0.02900}\) & 0.00 & \(\mathbf{-0.00855}\) & 0.00 & **0.03760** & 0.00 \\
**Importance of transport: Medium** & \(\mathbf{-0.15900}\) & 0.00 & \(\mathbf{-0.07270}\) & 0.01 & **0.23100** & 0.00 \\
**Importance of transport: High** & \(\mathbf{-0.23300}\) & 0.00 & \(\mathbf{-0.04960}\) & 0.00 & **0.28300** & 0.00 \\
**Accommodation zone: Central Almond** & \(\mathbf{-0.09830}\) & 0.00 & \(\mathbf{-0.05610}\) & 0.07 & **0.15400** & 0.02 \\
**Accommodation zone: Periphery** & \(\mathbf{-0.15100}\) & 0.00 & \(\mathbf{-0.12300}\) & 0.02 & **0.27400** & 0.00 \\
**Distance to metro** & \(-0.00011\) & 0.39 & \(-0.00003\) & 0.41 & 0.00014 & 0.39 \\
**Distance to bus** & 0.00048 & 0.11 & 0.00014 & 0.15 & \(-0.00063\) & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Marginal effects of the ordered logistic regression. Significant marginal effects at the 10% level are marked in bold.