## 2 Literature Review
To address this gap, meteorological data were added to the model as independent variables. Moreover, calendar-based factors such as public holidays and school days, along with temporal features were additionally integrated. Furthermore, the application of the NARX model in short-term passenger demand forecasting is rarely seen in the current literature, indicating a methodological limitation. Another significant shortcoming is the lack of studies that offer a comparative analysis of bus and rail passenger volumes at both regional and route-specific scales. This study aims to contribute to the literature by addressing these theoretical and methodological gaps. Prior to proceeding to the forecasting modeling, the XGBoost algorithm was employed for feature selection to evaluate the importance of variables and improve the efficiency of the model. Because of its capacity to model dynamic time-related dependencies and take into account both present and historical data, the LSTM network was selected as the main forecasting model in this study. Although numerous deep learning models have been built for forecasting passenger demand in public transportation, choosing the most fitting model for practical use remains a key problem. In this context, four models were compared, and their performances were assessed using the RMSE metric. Additionally, the Friedman test was used to determine the statistical significance of differences between the models, and recommendations for model selection were provided. As part of the application, the study aims to forecast passenger numbers for both bus and rail systems using an LSTM-based deep learning model. Ridership data were sourced from the Istanbul Metropolitan Municipality (IMM) IT Department, while meteorological data were obtained from the Turkish State Meteorological Service (MGM). By analyzing different data sources in a holistic way, the model built with these data seeks to deliver more realistic and reliable forecasts. This study offers significant advancements through four basic contributions:  
1. In order to enhance the accuracy of passenger flow forecasting for both bus and rail systems, the model includes not only time-dependent independent variables (hour, day, month, day of the week) but also external factors such as school days, public holidays, and atmospheric variables (temperature, precipitation, humidity, wind speed, cloudiness, and snowfall) that may influence ridership. 2. Prior to proceeding with the forecasting models, a feature importance analysis was conducted using the XGBoost algorithm to assess the significance of the independent variables incorporated in the model. This analysis enabled the identification of the most influential variables, and then, the feature selection process was conducted by determining the variables that most affected the efficiency of the model. 3. Passenger flow prediction was conducted on both regional and route-specific levels, covering rail and bus lines. Special attention was given to evaluating passenger behavior along routes shared by both modes of transport. 4. The prediction process was started with the LSTM model, followed by the application of advanced deep learning algorithms such as GRU, RNN, and NARX. The performance of these models was then evaluated through comparative analysis. The structure of the rest of this paper is as follows: Section 2 gives a comprehensive overview of the data utilized, the initial data handling procedures implemented, the evaluation metrics applied, and outlines the main methodological framework. Section 3 presents the results of the forecasting experiments for bus and rail passenger numbers, including a performance assessment of the models using appropriate error measurement criteria. Section 4 offers an in-depth discussion of the outcomes, addresses the study's limitations and the shortcomings of current approaches, and outlines recommendations for future investigations.