## 4 Model Testing and Results Analysis
### Results Analysis and Comparison
\begin{table}
\begin{tabular}{l l c c c c} \hline
**Dataset** & **MODEL** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **EV** \\ \hline \multirow{4}{*}{1} & CNN & 5.905 & 3.333 & 0.92 & 0.921 \\  & ConvLSTM & 8.064 & 5.445 & 0.852 & 0.887 \\  & BiGRU & 6.726 & 3.894 & 0.897 & 0.897 \\  & BiLSTM & 5.846 & 3.515 & 0.922 & 0.923 \\  & GRU & 6.627 & 4.087 & 0.9 & 0.902 \\  & LSTM & 6.29 & 3.878 & 0.91 & 0.912 \\  & GAHD-VAE & 3.336 & 2.81 & 0.975 & 0.979 \\ \hline \multirow{4}{*}{2} & CNN & 6.096 & 2.526 & 0.853 & 0.853 \\  & ConvLSTM & 5.058 & 2.655 & 0.899 & 0.9 \\  & GRU & 5.45 & 2.784 & 0.883 & 0.883 \\  & LSTM & 5.444 & 2.758 & 0.883 & 0.883 \\  & BiGRU & 6.239 & 2.639 & 0.847 & 0.847 \\  & BiLSTM & 5.874 & 2.531 & 0.864 & 0.864 \\  & GAHD-VAE & 2.693 & 1.681 & 0.971 & 0.972 \\ \hline \multirow{4}{*}{3} & CNN & 3.837 & 1.357 & 0.805 & 0.806 \\  & ConvLSTM & 4.482 & 3.332 & 0.735 & 0.782 \\  & BiGRU & 2.254 & 1.344 & 0.935 & 0.94 \\  & BiLSTM & 2.762 & 1.241 & 0.903 & 0.903 \\  & GRU & 3.263 & 2.039 & 0.864 & 0.864 \\  & LSTM & 4.414 & 2.964 & 0.751 & 0.752 \\  & GAHD-VAE & 1.586 & 0.97 & 0.968 & 0.968 \\ \hline \multirow{4}{*}{4} & CNN & 5.609 & 3.8 & 0.893 & 0.895 \\  & ConvLSTM & 7.307 & 4.681 & 0.819 & 0.842 \\  & BiGRU & 6.186 & 3.47 & 0.872 & 0.875 \\  & BiLSTM & 5.867 & 3.175 & 0.885 & 0.885 \\  & GRU & 6.582 & 3.736 & 0.855 & 0.855 \\  & LSTM & 6.566 & 3.819 & 0.856 & 0.857 \\  & GAHD-VAE & 4.053 & 3.077 & 0.945 & 0.954 \\ \hline \multirow{4}{*}{5} & CNN & 7.553 & 4.416 & 0.845 & 0.845 \\  & ConvLSTM & 8.872 & 6.59 & 0.756 & 0.801 \\  & BiGRU & 9.998 & 5.721 & 0.791 & 0.797 \\  & BiLSTM & 9.692 & 5.419 & 0.804 & 0.809 \\  & GRU & 10.763 & 6.197 & 0.758 & 0.76 \\  & LSTM & 10.746 & 6.355 & 0.759 & 0.768 \\  & GAHD-VAE & 5.641 & 4.853 & 0.933 & 0.952 \\ \hline \multirow{4}{*}{6} & CNN & 5.31 & 2.794 & 0.924 & 0.927 \\  & ConvLSTM & 5.887 & 3.958 & 0.905 & 0.91 \\  & BiGRU & 5.253 & 3.355 & 0.925 & 0.937 \\  & BiLSTM & 5.249 & 2.964 & 0.925 & 0.931 \\  & GRU & 5.959 & 3.505 & 0.904 & 0.911 \\  & LSTM & 6.061 & 3.883 & 0.9 & 0.916 \\  & GAHD-VAE & 2.824 & 1.867 & 0.978 & 0.978 \\ \hline \end{tabular}
\end{table} 
Table 4: One-step ahead forecasting of pedestrian and bicycle traffic flows using the seven deep learning models. \begin{table}
\begin{tabular}{l c c c c} \hline
**MODEL** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **EV** \\ \hline CNN & 5.72 & 3.04 & 0.87 & 0.87 \\ ConvLSTM & 6.61 & 4.44 & 0.83 & 0.85 \\ BiGRU & 6.11 & 3.40 & 0.88 & 0.88 \\ BiLSTM & 5.88 & 3.14 & 0.88 & 0.89 \\ LSTM & 6.59 & 3.94 & 0.84 & 0.85 \\ GRU & 6.44 & 3.72 & 0.86 & 0.86 \\ GAHD-VAE & 3.36 & 2.54 & 0.96 & 0.97 \\ \hline \end{tabular}
\end{table} 
Table 5: Averaged measurements of effectiveness per model. Table 7 displays the aggregated performances of GAHD-VAE per configurations of the attention mechanism (i.e., additive attention mode and multiplicative attention mode). \(R^{2}\) implies that the use of the two configurations in the GAHD-VAE approach results in good forecasting performance. Overall, forecasts based on the additive attention mode outperform those based on the multiplicative attention mode.