## 5 Model Evaluation
### Model Experiment 4: Weather Integrated Volume Prediction  
The results for this scenario demonstrated a high accuracy for the BiLSTM model above 96% for multiple prediction horizons. For the \"LSTM_No weather\" scenario, the results also showed a high level of accuracy above 93% for multiple prediction horizons up to 60 min into the future. In the second scenario, \"BiLSTM_Rain\" and \"LSTM_Rain\", the input data included two features: traffic volume and rain data. The results for the BiLSTM model maintained a similar accuracy above 96% for multiple prediction horizons and above 93% for the LSTM model for up to 60 min into the future. The third scenario, \"BiLSTM_Rain and Wind\" and \"LSTM_Rain and Wind\", incorporated three inputs: volume data with rain and wind speed. The results showed prediction accuracies above 96% and 93% for BiLSTM and LSTM, respectively. The last scenario, \"BiLSTM_Weather\" and \"LSTM_Weather\", involved five inputs: volume, rain, wind speed and humidity and air temperature. The results for both models demonstrated the robustness of the models when tested under multiple weather conditions with an accuracy above 96% for the BiLSTM model and 93% for the LSTM model (Table 6). It can also be noticed that incorporating weather data into BiLSTM led to a minor improvement in the accuracy for multiple prediction horizons. For example, the prediction increased the most when traffic volume was trained and tested with rainfall intensities for 15-min prediction horizons. For 60-min prediction horizons,  
Figure 16: Volume prediction results for 60-min horizons using the 4-BiLSTM model. Source: authors. Note: The first 500 conservative data points in the northbound direction were selected for presentation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Prediction Horizons**} & \multicolumn{5}{c}{**Speed (km/h)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**15 min**} & \multicolumn{2}{c}{**30 min**} & \multicolumn{2}{c}{**45 min**} & \multicolumn{2}{c}{**60 min**} \\ \cline{2-9}  & **MAPE (\%)** & **Accuracy** (\%)** & **MAPE (\%)** & **Accuracy** (\%)** & **MAPE (\%)** & **Accuracy** (\%)** \\ \hline BiLSTM\_No weather & 0.12 & 99.88 & 0.15 & 99.85 & 0.17 & 99.83 & 0.25 & 99.75 \\ \hline BiLSTM\_Rain & 0.40 & 99.60 & 0.28 & 99.72 & 0.41 & 99.59 & 2.02 & 97.98 \\ \hline BiLSTM\_Rain and Wind & 0.30 & 99.70 & 0.23 & 99.77 & 0.55 & 99.45 & 1.91 & 98.09 \\ \hline BiLSTM\_Weather & 0.21 & 99.79 & 0.32 & 99.68 & 0.32 & 99.68 & 0.23 & 99.77 \\ \hline LSTM\_No weather & 7.08 & 92.92 & 7.76 & 92.24 & 7.87 & 92.13 & 8.09 & 91.91 \\ \hline LSTM\_Rain & 7.40 & 92.60 & 8.07 & 91.93 & 8.32 & 91.68 & 8.47 & 91.53 \\ \hline LSTM\_Rain and Wind & 7.40 & 92.60 & 8.18 & 91.82 & 8.22 & 91.78 & 8.38 & 91.62 \\ \hline LSTM\_All Weather & 7.34 & 92.66 & 8.04 & 91.96 & 8.18 & 91.82 & 8.35 & 91.65 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speed prediction accuracies under multiple weather conditions results.  
volume with rain and wind improved the accuracy to 97.13% when compared to 96.86% without weather information. However, the overall results demonstrated the robustness of the model in achieving high prediction accuracy when trained/tested under variable weather conditions. The green cells represent the best-performing models, while the yellow cells represent the worst-performing models.