## 4 Model Testing and Results Analysis
### Results Analysis and Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Type** & **Attention** & **RMSE** & **MAE** & \(R^{2}\) & **EV** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{\(\bigotimes\)} & Tanh & 2.663 & 1.639 & 0.972 & 0.973 \\  & & Sigmoid & 1.812 & 1.224 & 0.987 & 0.988 \\  & & Relu & 2.086 & 1.457 & 0.983 & 0.985 \\  & & None & 5.079 & 3.92 & 0.941 & 0.951 \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{\(\bigoplus\)} & Tanh & 3,336 & **2.81** & **0.975** & **0.979** \\ \cline{2-6}  & & Sigmoid & 2.225 & 1.686 & 0.98 & 0.981 \\  & & Relu & 1.841 & 1.397 & 0.987 & 0.987 \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{\(\bigotimes\)} & Tanh & 2.072 & 1.601 & 0.983 & 0.984 \\  & & Sigmoid & 2.281 & 1.931 & 0.979 & 0.984 \\  & & Relu & 2.373 & 2.094 & 0.978 & 0.99 \\  & & None & 2.403 & 1.692 & 0.977 & 0.979 \\ \hline \hline \multirow{3}{*}{2} & \multirow{3}{*}{\(\bigoplus\)} & Tanh & 2.393 & **1.616** & 0.972 & 0.98 \\  & & Sigmoid & 1.81 & 1.273 & 0.987 & 0.988 \\  & & Relu & 2.399 & 1.736 & 0.977 & 0.978 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{\(\bigotimes\)} & Tanh & 1.844 & 1.587 & 0.957 & 0.973 \\  & & Sigmoid & 1.86 & 1.262 & 0.956 & 0.959 \\  & & Relu & 1.878 & 1.397 & 0.955 & 0.962 \\  & & None & 1.753 & 1.105 & 0.961 & 0.962 \\ \hline \hline \multirow{3}{*}{3} & \multirow{3}{*}{\(\bigoplus\)} & Tanh & 1.586 & **0.97** & **0.968** & **0.968** \\  & & Sigmoid & 2.299 & 1.955 & 0.932 & 0.958 \\  & & Relu & 1.705 & 0.797 & 0.963 & 0.964 \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{\(\bigotimes\)} & Tanh & 1.18 & 0.761 & 0.986 & 0.986 \\  & & Sigmoid & 2.137 & 1.637 & 0.953 & 0.957 \\  & & Relu & 2.259 & 1.769 & 0.947 & 0.956 \\  & & None & 2.754 & 2.205 & 0.922 & 0.953 \\ \hline \hline \multirow{3}{*}{4} & \multirow{3}{*}{\(\bigoplus\)} & Tanh & 4.053 & 3.077 & 0.945 & 0.954 \\  & & Sigmoid & 2.066 & 1.932 & 0.956 & 0.987 \\  & & Relu & 2.591 & 2.021 & 0.931 & 0.958 \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{\(\bigotimes\)} & Tanh & 3.469 & 3.091 & 0.939 & 0.965 \\  & & Sigmoid & 2.743 & 1.969 & 0.962 & 0.968 \\  & & Relu & 3.979 & 2.63 & 0.92 & 0.93 \\  & & None & 2.784 & 2.417 & 0.961 & 0.978 \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{\(\bigoplus\)} & Tanh & 5.641 & **4.853** & **0.933** & **0.952** \\ \cline{2-6}  & & Sigmoid & 3.672 & 2.448 & 0.932 & 0.936 \\  & & Relu & 3.508 & 2.796 & 0.938 & 0.952 \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{\(\bigotimes\)} & Tanh & 3.118 & 2.095 & 0.974 & 0.975 \\  & & Sigmoid & 3.077 & 2.534 & 0.974 & 0.98 \\  & & Relu & 2.921 & 1.661 & 0.977 & 0.977 \\  & & None & 4.481 & 3.192 & 0.946 & 0.958 \\ \hline \hline \multirow{3}{*}{6} & \multirow{3}{*}{\(\bigoplus\)} & Tanh & 2.824 & **1.867** & **0.978** & **0.978** \\  & & Sigmoid & 2.852 & 1.973 & 0.978 & 0.978 \\  & & Relu & 4.087 & 2.744 & 0.955 & 0.962 \\ \hline \end{tabular}
\end{table} 
Table 6: Evaluation of the forecasting performance of the GAHD-VAE under different configurations of the attention mechanism. \(\bigoplus\) is the additive attention mode, while \(\bigotimes\) is the multiplicative attention mode. The following experiments are devoted to assessing the proposed approach's daily forecasting performance against the other recurrent models. Table 8 summarized the results of forecasting daily pedestrian and bicycle traffic flows using the seven deep learning models based on the six datasets. Results indicate that the proposed approach scored the lowest averaged forecasting error (i.e., RMSE = 42 and MAE = 32) and the highest determination factor (i.e., \(R^{2}\) = 0.9 and EV = 0.9). Moreover, results in Table 8 indicate that the bi-directional recurrent neural networks (BiLSTM and BiGRU) exhibit higher accuracy compared to the uni-directional (LSTM, GRU). This could be due to the capability of BiLSTM and BiGRU in processing data in the forward and backward direction, which enable them to discover more complex features. We also observe that BiLSTM outperforms BiGRU slightly; however, LSTM and GRU recorded mostly the same score.