## 6 Conclusions  
This study has revealed that specific weather parameters can significantly impact passenger demand forecasts in public transportation systems. Employing 1.5 years of smart card and meteorological data from 2021 to 2022, passenger numbers for both bus and rail systems were estimated through deep learning models for Istanbul's Asian side and the Kadkoy-Pendik corridor. The key findings are summarized below:  
1. Deep learning-based models have produced successful results, particularly in predicting both bus and rail passenger flows.
2. The highest forecasting accuracy among weather variables was achieved when both temperature and wind speed from one hour earlier were included in the model. However, when considered alone, these parameters did not demonstrate significant effects when considered independently.
3. The models produced more stable predictions during weekdays and peak hours, while forecasting performance dropped during weekends and off-peak periods due to increased fluctuations in passenger volume.
4. Route-based rail models that include temperature and wind speed provided a modest improvement in RMSE of 2.4%.
5. In region-based bus forecasts, a modest 2.2% reduction in RMSE was observed on the Asian side, while a larger improvement of 3.69% was achieved on the Kadkoy-Pendik route.  
The main aim of the study was to develop short-term forecasts for bus and rail passenger volumes along the same region and route, including weather conditions. The incorporation of weather variables enhanced the prediction performance for buses; however, their effect on the rail system was minimal. In future studies, the timing of mass events such as football matches and concerts could be incorporated as external variables to mitigate sudden fluctuations during weekends and low-demand periods. Furthermore, using smart card data with minute-level granularity could improve model sensitivity, while extending the time span of the dataset would enhance the model's ability to capture seasonal effects. The use of model explanation techniques such as SHAP (SHapley Additive exPlanations) alongside XGBoost-based feature selection could provide a more comprehensive understanding of feature contributions. As a result, this integration may enhance the interpretability and transparency of forecasting models, thereby enabling a more accurate assessment of both model reliability and performance [75]. The findings support transportation planners in making more informed, data-driven decisions regarding scheduling, capacity management, and service optimization.  
**Author Contributions:** Conceptualization: C.Y. and A.P.A.; methodology: C.Y. and A.P.A.; software: C.Y.; validation: C.Y. and A.P.A.; formal analysis: C.Y.; investigation: A.P.A. and C.Y.; data curation: C.Y.; writing--original draft preparation: C.Y.; writing--review and editing: C.Y. and A.P.A.; visualization: C.Y.; supervision: A.P.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.