## 4 Data Collection and Analysis
This variable is a good indicator of the expected road congestion of each ride. Categorical variables that control time-related features for requested trips, such as month and hour (time of day), were included to capture changes in ride-hailing fares at different times. In addition, holidays and peak hours in Madrid were verified, obtaining the variables of the business day (which does not include weekends and holidays) and peak hours (on business days from 07:00 to 09:00 and 18:00 to 20:00, according to EMESA, 2019 [29]). Weather conditions are included in the analysis because they can affect Uber demand and thus influence ride fares [30]. Data on rain precipitation (measured in millimeters) was collected over 1 h. The State Meteorological Agency (AEMET) of Spain provided that information. To account for special events that affect its main competitor's transportation supply, the analysis also considers taxi strikes that occurred during the analysis period. A categorical variable was included to capture Madrid's strike days from September 2018 to June 2019. Taxi strikes occurred for 20 days during the period studied. Table 2 shows the descriptive statistics for the final data sample. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Typology** & **Unit** & \multicolumn{2}{c}{**Summary Statistics**} \\ \hline \multirow{4}{*}{UberX fare (FARE)} & \multirow{4}{*}{Continuous} & Euro (EUR) & Mean & 18.91 \\  & & & Median & 18.00 \\  & & Euro & Max. & 149.00 \\  & & & Min. & 3.50 \\  & & & SD & 8.14 \\  & & & Mean & 11.16 \\ Trip distance & \multirow{4}{*}{Continuous} & Kilometers & Median & 10.80 \\  & & (Km) & Max. & 36.97 \\  & & & Min. & 2.03 \\  & & & SD & 5.05 \\  & & & Mean & 18.32 \\ Travel time & \multirow{4}{*}{Continuous} & Minutes & Median & 18.00 \\ (TTIME) & & (min) & Max. & 53.00 \\  & & & Min. & 5.00 \\  & & & SD & 5.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary statistics of explanatory variables. Figure 2 shows the pair plot of continuous variables. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Typology** & **Unit** & \multicolumn{2}{c}{**Summary Statistics**} \\ \hline  & & & Mean & 4.57 \\ Delay & Continuous & Minutes & Median & 4.00 \\  (DELAY) & Continuous & (min) & Max. & 32.00 \\  & & (min) & Min. & 0.00 \\  & & & SD & 3.29 \\  & & & Mean & 0.14 \\ Rain precipitation & & Millimeter & Median & 0.00 \\  (PREC) & Continuous & (mm) & Max. & 91.00 \\  & & & Min. & 0.00 \\  & & & SD & 1.64 \\ Business day & & Business day & 448,300 \\ (BUSNESS\_DAY) & Categorical & - & Not business day & 217,677 \\ Peak hour & & Peak hour & 110,709 \\ (PEAK\_HOUR) & Categorical & - & Not peak hour & 555,268 \\ Tax strike & & & Strike & 42,333 \\ (STRIKE) & Categorical & - & No strike & 623,644 \\ \hline \end{tabular} Note: The Month (MONTH) and Hour (HOUR) variables were also considered in this research, being included as dummy variables.  
\end{table} 
Table 2: \(Cont.\)The rain precipitation variable was highly skewed (\(\gamma\)1 = 23.59) and has 653,048 (98.1%) zeros. The data out of the main diagonal represent scatter plots of continuous variables, in which the correlation between them can also be observed. Figure 3 shows the boxplot of continuous variables. It is worth noting that no outlier data were removed during the data cleaning process. \"Extreme\" ride-hailing fares were kept in the dataset because it is believed they represented some type of instant \"market irregularity\", due to either low supply in cases of high demand or the opposite. Figure 4 shows the average fare per hour and type of day (business, weekends, and holidays) for a better understanding of the fare trend throughout the day. Uber fares on business days appear to have a morning peak at 08 h and a smoother and wider afternoon peak at around 18 h. Weekend and holiday fares, on the other hand, are more expensive late at night (which can be linked to leisure trips back home) and in the early afternoon (which can be linked to leisure activities such as dining, shopping, etc.).