## 4 Experimental Results  
According to Table 6, in the LSTM prediction model developed using combinations of weather parameters for the bus lines operating along the Kadikoy-Pendik route, the inclusion of temperature and wind speed values from one hour prior led to a 3.7% reduction in RMSE compared to the model without weather variables. Higher MAPE values were observed during weekends and off-peak hours, a pattern also found in rail systems. In contrast, weekday and peak-hour predictions demonstrated more stable and balanced estimates. In Tables 7-11, the symbol \(\checkmark\) indicates the presence of a variable, X denotes its absence, and bold values highlight the best performance results. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multicolumn{8}{c}{**Input Variables**} & \multicolumn{8}{c}{**LSTM**} \\ \hline
**Model** & **Temporal** & **xTemp** & **xHumidity** & **xWind** & **MAPE** & **RMSE** & **TIC** & **STD\_AE** & **RAE** & **STD\_APE** \\ \hline
1 & \(\checkmark\) & X & X & X & 0.0508 & 562 & 0.0249 & 378 & 0.107 & 6.504 \\
2 & \(\checkmark\) & \(\checkmark\) & X & X & 0.0503 & 560 & 0.0248 & 374 & 0.106 & 5.863 \\
3 & \(\checkmark\) & \(\checkmark\) & X & \(\checkmark\) & **0.0491** & **553** & **0.024** & **371** & **0.105** & **5.955** \\
4 & \(\checkmark\) & X & X & \(\checkmark\) & 0.051 & 568 & 0.0269 & 384 & 0.108 & 5.99 \\
5 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 0.0539 & 615 & 0.0274 & 413 & 0.116 & 6.724 \\
6 & \(\checkmark\) & X & \(\checkmark\) & \(\checkmark\) & 0.052 & 569 & 0.0253 & 376 & 0.109 & 6.462 \\
7 & \(\checkmark\) & X & \(\checkmark\) & \(\checkmark\) & 0.0533 & 589 & 0.0262 & 393 & 0.11 & 7.146 \\
8 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 0.0546 & 601 & 0.0266 & 389 & 0.1177 & 6.351 \\ \hline \end{tabular}
\end{table} 
Table 6: Public bus transportation on the Kadikóy–Pendik route. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{1}{c}{\multirow{2}{*}{**No**}} & \multicolumn{2}{c}{**Best Tuning Results**} \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{**LSTM-GRU**} & \multicolumn{1}{c}{**RNN**} \\ \hline
1 & epochs & \(100\) & \(100\) \\
2 & batch size & \(32\) & \(32\) \\
3 & activation & tanh & tanh \\
4 & recurrent\_activation & sigmoid & - \\
5 & learning rate & \(0.001\) & \(0.001\) \\
6 & hidden nodes & (\(256,128,64\)) & (\(128,64\)) \\
7 & number of hidden layers & \(3\) & \(2\) \\
8 & optimizer & adam & adam \\
9 & dropout & \(0.2\) & \(0.2\) \\
10 & timestep & \(10\) & \(10\) \\
11 & early stopping monitor & val\_loss & val\_loss \\
12 & early stopping patience & \(10\) & \(10\) \\
13 & learning rate reduction patience & \(5\) & \(5\) \\
14 & reduction factor & \(0.2\) & \(0.2\) \\ \hline \end{tabular}
\end{table} 
Table 4: Optimum hyperparameters for the models. \begin{table}
\begin{tabular}{c c} \hline
**No** & **Parameters** & **NARX** \\ \hline
1 & activation & tansig (hidden), purelin (output) \\
2 & hidden nodes & (\(20,20,20\)) \\
3 & number of hidden layers & \(3\) \\
4 & optimizer & trainlm (Levenberg–Marquardt) \\
5 & timestep & \(10\) \\ \hline \end{tabular}
\end{table} 
Table 5: Optimum hyperparameters for the NARX model. Table 7 presents findings for two rail systems (M4 Metro and B1 Gebze-Uskudar Line) along the Kadkoy-Pendik corridor. In scenarios where temperature and wind speed from one hour earlier were included in the LSTM model, the RMSE reached its lowest level, achieving a 2.5% improvement over models using only temporal variables. However, an increase in MAPE was noted during weekends and off-peak hours, suggesting higher percentage error rates during those periods. Additionally, the RMSE values obtained for bus passenger flow predictions were found to be lower than those for rail lines, indicating that bus traffic might be more responsive to short-term fluctuations and thus more effectively modeled.