## 3 Materials and Methods
### Parameter Acquisition and Preprocessing  
To estimate hourly bus passenger numbers, in addition to temporal data, meteorological data, as well as information concerning public holidays and school days, were used. In DL models, the correct selection of input variables is vital for the model's success. Hence, to improve the accuracy of passenger number estimations, weather conditions for the corresponding time slots were also included in the analysis, and their effects on bus and rail systems were analyzed. In this study, two different sets of independent variables were generated. The first dataset includes time-based parameters along with public holiday and school day variables. The second dataset contains only meteorological parameters. The dependent variable (output) is the hourly passenger number for buses and rail systems. Passenger count data were retrieved from the Istanbul Metropolitan Municipality database and consist of hourly recorded boarding data throughout the year via smart card systems. The study is restricted to the Asian side of Istanbul; the transportation network map for this region is presented in Figure 1. The initial phase of the study was conducted on a regional scale, calculating the number of passengers using the bus and rail systems across the entire Asian side. In the second phase of the study, conducted on a route-specific level, two rail lines were examined: the Kadikov-Kartal metro and the Gebze-Uskudar commuter line. Bus lines operating along the routes of these two rail lines were identified and incorporated into the analysis. Specifically, this approach aimed to enable a relative evaluation of bus lines sharing the same transportation corridor with the rail systems. Figure 2 visualizes the two rail lines in red and black, and the 36 bus lines overlapping with these routes are shown in blue. This map depicts the spatial coverage of the study and the geographical distribution of the transportation lines analyzed. The second database used in the study comprises hourly meteorological parameters documented between 1 July 2021 and 31 January 2023 by the Turkish State Meteorological Service. This collection contains hourly readings of average temperature (\({}^{\circ}\)C), precipitation (mm), humidity (%), wind speed (m/s), cloudiness (okta), and snow depth (cm) for the  
Figure 1: Asian side of Istanbul bus lines. Figure 2: Subway and bus lines on the Kadiköy–Pendik route. Asian side. Since the route-specific analysis concentrates on the Kadikov-Pendik line, two meteorological stations situated along the route--station 17064 (Istanbul-Regional) and station 17813 (Kadikov-Goztepe)--were included in the study. Histogram charts displaying the distribution of meteorological data are presented in Figure 3. Temperature ranges approximately between 0 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, demonstrating a bimodal pattern reflecting seasonal variations. Most precipitation values are near zero. Wind speed displays a right-skewed spread, indicating that lower wind speeds are more widespread. Humidity mirrors a trend close to normal, concentrated between 50% and 80%. For cloudiness, clear skies (0 okta) and complete overcast circumstances (8 okta) are most frequently observed. Snow depth, like rainfall, predominantly hovers near zero. Descriptive statistical graphs of the weather factors used in the study are shown in Table 1. The data comprise 10,440 hourly observations, including meteorological variables such as temperature, precipitation, wind speed, humidity, cloudiness, and snow depth. Meteorological parameters, public holidays, and school days were merged to form input variables comprising 10,440 rows. As for school days, the data span the school calendar for primary, middle, and high schools under the 12-year compulsory education system. Due to the large number of universities in Istanbul and the variety of their academic calendars, university school days were excluded. Figures 4 and 5 provide contour maps visualizing the standard hourly ridership figures for bus lines and railway networks.