# Modeling Public Transportation Use Among Short-Term Rental Guests in Madrid
## 2 Materials and Methods
### Tourist Profiles  
Prior to modeling the level of use of public transportation by tourists, an exploratory analysis of the processed data was carried out. This analysis covers the values of the independent variables considering the overall sample and the values considering the different levels of the dependent variable (low use, mid use, and high use of public transport). Table 4 provides descriptive statistics for the variables included in the first model, which examines the overall use of public transport (metro and bus combined). The sample consists of 610 tourists, of whom 25.4% were classified as low users, 37.9% as medium users, and 36.7% as high users of public transport. A slight majority of tourists (56.2%) were return visitors to the city. Among them, 39.1% were high users of public transport, compared to 33.7% of first-time visitors, suggesting that prior familiarity with the city may encourage greater use of local transit. Regarding group type, most tourists traveled in a group (68.5%). Solo travelers, though a small segment (4.8%), had the highest proportion of high public transport users (44.8%), perhaps reflecting greater independence or reliance on transport when traveling alone. Geographic origin also reveals differences in travel behavior. Tourists from America exhibited the highest share of high users (44.3%), followed by those from Spain (39.0%). In contrast, European visitors had a slightly lower rate of high use (31.0%). Most tourists arrived by plane (72.6%). Among these, 36.3% were high users of public transport. Inter \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Unit** & **Type** & **Definition/Categories** \\ \hline
**Dependent variable** & & \\ \hline Use of public transport & Categorical & (1) Low use, (2) medium use, and (3) high use \\ Use of bus & Categorical & (1) Low use, (2) medium use, and (3) high use \\ Use of metro & Categorical & (1) Low use, (2) medium use, and (3) high use \\ \hline
**Independent variables** & & \\ \hline
**Tourist data** & & \\ \hline First time in the city & Categorical & Is it the first time visiting the city? (yes/no) \\ Type of group & Categorical & Solo, couple, and group \\ Zone of the world & Categorical & Spain, Europe, America, and the rest of the world \\ Access to the city & Categorical & Public land-based (train and bus) transport, plane, and private car \\ Number of nights & \# & Numerical & The number of nights in the city \\ Visited touristic POIs & \# & Numerical & The number of visited touristic POIs \\ Importance of public transport & Categorical & The importance given to the location of bus stops and/or metro \\  & & stations when choosing the accommodation; Likert 5-level scale \\  & & categorization: low (1–2), medium (3), high (4–5) \\ \hline
**Accommodation data** & & \\ \hline Accommodation zone & Categorical & Zone of the city assigned using the declared postal address: (1) city \\ Distance to bus stop & m & Categorical & Center, (2) central almond, (3) periphery \\ Distance to metro station & m & Numerical & Euclidean distance from the accommodation to the nearest \\  & & & metro station \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variables included in this study. estingly, the highest proportion of high users (40.3%) was among those who arrived via land-based transport (e.g., train or bus), possibly indicating a general preference for public modes. Tourists who arrived by private vehicle had the lowest rate of high use (28.9%). The length of stay averaged 3.94 nights, with high users staying longer on average (4.45 nights) than low or medium users. Likewise, tourists who visited more points of interest also tended to use public transport more: high users visited 7.05 POIs on average, compared to 5.75 among low users. The importance assigned to public transport when choosing accommodation declared by tourists was strongly related to their usage level.