## 5 Discussion  
The analyses revealed that the humidity parameter did not make a significant contribution to passenger demand prediction performance in any of the models. Similarly, temperature had a limited effect, particularly in bus passenger forecasting. In contrast, when wind speed was incorporated into the model alongside temperature with a one-hour lag, it led to a 2.2% reduction in RMSE at the regional level for bus forecasts on the Asian side and a 2.4% reduction for rail systems at the route level. While these results indicate slight improvements, they suggest that the influence of weather variables is generally limited in such contexts. However, on the Kadikoy-Pendik route, the same combination resulted in a 3.69% decrease in RMSE, representing a more meaningful enhancement in predictive accuracy. This finding highlights that, although the overall contribution of weather-related variables may be modest, their impact can become more pronounced under specific spatial and operational conditions. It should also be noted that the dataset used in this study covers only a 1.5-year time span, which limits the ability to identify statistically significant seasonal cycles. As such, the full potential of weather-related variables in improving model accuracy may not have been fully realized. One possible explanation for the relatively smaller impact of weather variables on railway passenger flow compared to bus systems could be the difference in infrastructure. Unlike open-air bus stops, train stations generally provide sheltered environments that protect passengers from adverse weather conditions. Moreover, while accessing bus stops often requires walking through open or unprotected areas, train stations are typically integrated into larger transportation hubs or enclosed structures, offering greater protection and comfort regardless of the weather. In particular, passengers using underground metro stations are less likely to be affected by extreme weather events due to the enclosed nature of the infrastructure [71,72]. The limited impact of precipitation- and snow-related variables observed in this study may be attributed to the relatively low number of rainy and snowy days in the dataset. This data scarcity may have restricted the model's ability to learn the relationship between such weather conditions and passenger flow, resulting in their low ranking in the feature importance analysis. However, in regions with higher levels of precipitation, the impact of these variables on model performance may be more substantial. To address these gaps, future studies conducted in different climatic settings and based on longer data periods could better capture the influence of adverse weather conditions on public transport demand. Across all models, the StdAPE values were higher than the MAPE values. This discrepancy can be linked to more noticeable deviations in scenarios with low passenger volumes. Lowering StdAPE would contribute to more consistent forecasts. The analysis also stressed that error rates were proportionally higher during off-peak periods, suggesting that special attention should be given to enhancing predictions during these hours. Among the four models built for predicting bus and rail passenger volumes, the NARX outperformed the others, yielding the lowest MAPE and RMSE values. These findings indicate the superior predictive accuracy of the NARX model in short-term forecasting tasks. Uncertainty in weather forecasts can also directly affect the accuracy of passenger flow predictions. While short-term forecasts (1-7 days: high accuracy; 8-10 days: moderate accuracy; 11-14 days: low accuracy) are generally more reliable [73], they can introduce significant uncertainty in long-term scenarios. Consequently, the models developed in this study are best suited for short-term forecasting applications. Certain limitations should be acknowledged.