# Modeling Public Transportation Use Among Short-Term Rental Guests in Madrid
## 2 Materials and Methods  
### Methodological Workflow  
This study followed a five-step methodological workflow, summarized in Figure 1, combining structured data collection with statistical analysis to examine factors influencing public transport (PT) use among guests staying in short-term rentals in Madrid. The methodological approach consists of the following steps:  
* **Step 1: Data collection**. A structured face-to-face survey was administered throughout 2024 to visitors staying in STRs. This step is further explained in Section 2.2.
* **Step 2: Data processing**. Survey responses were cleaned, standardized, and translated. A detailed geocoding process was applied to postal addresses, allowing the spatial assignment of each STR to one of three city zones (city center, central almond, or periphery). Independent and dependent variables were constructed based on survey responses, including transport usage, demographic profiles, and travel behavior. This step is covered in Section 2.3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Tourist Type** & **Case Study** & **Dependent Variable** & **Model** & **Location of the Accommodation** & **PT Accessibility** \\ \hline
[15] & Visitors staying overnight & Barcelona & Most used mode of transport within the city & Multinomial logit & No & No \\ \hline
[25] & University students & - & Share of sustainable mobility at destination & Tobit & No & No \\ \hline
[21] & German and Italian visitors & South Tyrol, Italy & PT use (5-point scale) & Ordered logit & No & No \\ \hline
[24] & All visitors & 10 European capitals & Sustainable mobility (5-point scale) & Ordered logit & Yes & No \\ \hline
[22] & All visitors & 10 European capitals & PT use (3-point scale) & PLS-SEM & Yes & No \\ \hline Our study & Visitors staying at STRs & Madrid (urban) & PT use (3-point scale) & Ordered logit & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
* **Step 3: Data exploration**. A descriptive analysis of the sample was conducted to identify initial relationships between PT usage and tourist or accommodation characteristics. This step is covered in Section 2.4.
* **Step 4: Data modeling**. An ordered logistic regression model was used to analyze the ordinal outcome variable representing levels of PT usage (low, medium, and high), as well as separate models for bus and metro usage. Predictor variables included visitor characteristics, travel behavior, and accommodation attributes. Spatial data, including distances to transport nodes, were integrated into the models. This step is further explained in Section 2.5.
* **Step 5: Interpretation**. Model coefficients and marginal effects were interpreted to identify the key drivers of PT usage. These findings were then contextualized in terms of policy implications for sustainable tourism and STR decentralization. This step is covered in Sections 3 and 4.  
This integrative workflow allows for a data-driven analysis of STR guest mobility and contributes to the literature by combining detailed survey data, the spatial location of accommodations, and transport accessibility metrics in a statistical modeling framework.