## 5 Model Evaluation
### Model Experiment 3: Weather Integrated Speed Prediction  
The weather conditions incorporated in this experiment included rainfall observation in mm, air temperature in degrees Celsius, relative humidity in percentage % and wind speed in km/h. Weather data were gathered at 1-min intervals, and speed data were gathered every 15 min; the weather data were then aggregated into 15-min intervals to match the frequency of the traffic data. These models achieved the highest performance in the previous experiments when tested in the northbound and southbound directions. Hence, multiple BiLSTM and LSTM scenarios were trained/tested under different weather conditions to evaluate the model's robustness. The total observations used in all scenarios \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Volume (No. of Vehicles) Northbound**} \\ \cline{2-11}
**Prediction** & \multicolumn{3}{c}{**BiLSTM**} & \multicolumn{3}{c}{**Uni-LSTM**} & \multicolumn{3}{c}{**RNN**} & \multicolumn{3}{c}{**Elman**} & \multicolumn{3}{c}{**DLBP**} \\ \cline{2-11}
**Horizons** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
15 min & 0.54\% & 99.46\% & 4.77\% & 95.23\% & 7.32\% & 92.68\% & 7.30\% & 92.70\% & 8.38\% & 91.62\% \\ \hline
30 min & 2.37\% & 97.63\% & 5.12\% & 94.88\% & 12.41\% & 87.59\% & 12.34\% & 87.66\% & 11.68\% & 88.32\% \\ \hline
45 min & 3.20\% & 96.80\% & 5.13\% & 94.87\% & 18.17\% & 81.83\% & 17.86\% & 82.14\% & 17.58\% & 82.42\% \\ \hline
60 min & 3.18\% & 96.82\% & 5.92\% & 94.08\% & 24.46\% & 75.54\% & 24.03\% & 75.97\% & 21.45\% & 78.55\% \\ \hline \multicolumn{11}{c}{Volume (No. of Vehicles) Southbound} \\ \cline{2-11} Prediction & \multicolumn{3}{c}{BiLSTM} & \multicolumn{3}{c}{LSTM} & \multicolumn{3}{c}{**RNN**} & \multicolumn{3}{c}{**Elman**} & \multicolumn{3}{c}{**DLBP**} \\ \cline{2-11}
**Horizons** & \multicolumn{3}{c}{MAPE} & \multicolumn{3}{c}{Accuracy} & \multicolumn{1}{c}{MAPE} & \multicolumn{1}{c}{Accuracy} & \multicolumn{1}{c}{MAPE} & \multicolumn{1}{c}{Accuracy} & \multicolumn{1}{c}{MAPE} & \multicolumn{1}{c}{Accuracy} \\ \cline{2-11}
**(\%)** & \multicolumn{3}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} & \multicolumn{1}{c}{(\%)} \\ \hline
15 min & 1.07\% & 98.93\% & 6.80\% & 93.20\% & 9.43\% & 90.57\% & 9.38\% & 90.62\% & 9.86\% & 90.14\% \\ \hline
30 min & 1.24\% & 98.76\% & 7.67\% & 92.33\% & 15.23\% & 84.77\% & 14.90\% & 85.10\% & 13.94\% & 86.06\% \\ \hline
45 min & 2.15\% & 97.85\% & 8.67\% & 91.33\% & 22.18\% & 77.82\% & 21.32\% & 78.68\% & 23.58\% & 76.42\% \\ \hline
60 min & 3.87\% & 96.13\% & 9.87\% & 90.13\% & 28.67\% & 71.33\% & 29.05\% & 70.95\% & 26.96\% & 73.04\% \\ \hline \multicolumn{11}{c}{Note: The green cells represent the best-performing models, while the yellow cells represent the worst-performing models.} \\ \hline \end{tabular} Note: The green cells represent the best-performing models, while the yellow cells represent the worst-performing models.  
\end{table} 
Table 4: Volume prediction accuracies results for northbound and southbound directions. Figure 13: Volume prediction results for 15-min horizons using the 4-BiLSTM model. Source: authors. Note: The first 500 conservative data points in the northbound direction were selected for presentation. were 35,036 observations for speed and weather information, with 21,022 observations used for training and 14,014 observations used for testing/validation. In the first scenario, \"BiLSTM_No weather\", the input only included the average speed (northbound and southbound directions). The results for this scenario demonstrated a high accuracy for the BiLSTM model above 99% for multiple prediction horizons.