## 5 Experimental Analysis
### Analysis of Results
#### 5.3.3 TaxiNYC  
As mentioned earlier, the TaxiNYC case study was created specifically to be able to evaluate the behavior of the proposed model in a wider set of scenarios than the literature. Consequently, in order to make a fair comparison, it was necessary to search for the best configuration of hyperparameters not only for the STREED-Net model but also for all the other approaches considered. The optimized parameters and the relative values used in the training phase are briefly summarized below for each model. Unreported configuration values have been set as for the BikeNYC case study since the two experiments share the same map size (\(16\times 8\)). The parameters for each model are as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **APE** \\ \hline HA & 6.56 & 26.46 & 4.09 \(\cdot 10^{5}\) \\ ST-ResNet & \(5.01\pm 0.07\) & \(21.97\pm 0.26\) & \(3.40\cdot 10^{5}\) \(\pm\) 4.06 \(\cdot 10^{5}\) \\ MST3D & \(4.98\pm 0.05\) & \(22.03\pm 0.47\) & \(3.41\cdot 10^{5}\) \(\pm\) 7.26 \(\cdot 10^{5}\) \\
3D-CLoST & \(4.90\pm 0.04\) & \(21.38\pm 0.20\) & \(3.31\cdot 10^{5}\) \(\pm\) 3.12 \(\cdot 10^{5}\) \\ PredCNN & \(4.81\pm 0.04\) & \(21.38\pm 0.24\) & \(3.31\cdot 10^{5}\) \(\pm\) 3.76 \(\cdot 10^{5}\) \\ ST-3 DNet & \(4.75\pm 0.06\) & \(21.42\pm 0.28\) & \(3.31\cdot 10^{5}\) \(\pm\) 4.36 \(\cdot 10^{5}\) \\ STAR & \(4.73\pm 0.05\) & \(20.97\pm 0.13\) & \(3.24\cdot 10^{5}\) \(\pm\) 2.02 \(\cdot 10^{5}\) \\ ACFM & \(4.68\pm 0.13\) & \(20.98\pm 0.68\) & \(3.25\cdot 10^{5}\) \(\pm\) 1.05 \(\cdot 10^{5}\) \\ \hline STREED-Net & \(\mathbf{4.67\pm 0.03}\) & \(\mathbf{20.85\pm 0.15}\) & \(\mathbf{3.23\cdot 10^{5}\pm 2.31\cdot 10^{5}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results obtained for the Bike NYC data set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **APE** \\ \hline HA & 40.93 & 30.96 & 6.77 \(\cdot 10^{7}\) \\ ST-ResNet & \(17.56\pm 0.91\) & \(15.74\pm 0.94\) & \(3.45\cdot 10^{7}\) \(\pm\) 2.05 \(\cdot 10^{6}\) \\ MST3D & \(21.34\pm 0.55\) & \(22.02\pm 1.40\) & \(4.81\cdot 10^{7}\) \(\pm\) 3.03 \(\cdot 10^{5}\) \\
3D-CLoST & \(17.10\pm 0.23\) & \(16.22\pm 0.20\) & \(3.55\cdot 10^{7}\) \(\pm\) 4.39 \(\cdot 10^{5}\) \\ PredCNN & \(17.42\pm 0.12\) & \(15.69\pm 0.17\) & \(3.43\cdot 10^{7}\) \(\pm\) 3.76 \(\cdot 10^{5}\) \\ ST-3 DNet & \(17.29\pm 0.42\) & \(15.64\pm 0.52\) & \(3.43\cdot 10^{7}\) \(\pm\) 1.13 \(\cdot 10^{6}\) \\ STAR & \(16.25\pm 0.40\) & \(15.40\pm 0.62\) & \(3.38\cdot 10^{7}\) \(\pm\) 1.36 \(\cdot 10^{6}\) \\ ACFM & \(15.67\pm 0.23\) & \(15.16\pm 0.33\) & \(3.32\cdot 10^{7}\) \(\pm\) 7.25 \(\cdot 10^{5}\) \\ \hline STREED-Net & \(\mathbf{15.61\pm 0.11}\) & \(\mathbf{14.73\pm 0.21}\) & \(\mathbf{3.22\cdot 10^{7}\pm 4.51\cdot 10^{5}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained for the Taxi Beijing data set. * **ST-ResNet*. ** Optimized parameters: number of residual units, batch size and learning rate. Optimal values found: 2, 16 and 0.0001. * **MST3D. ** Optimized parameters: batch size and learning rate. Optimal values found: 16 and 0.00034. * **PredCNN. ** Optimized parameters: encoder length, decoder length, number of hidden units, batch size and learning rate. Optimal values found: 2, 3, 64, 16 and 0.0001. * **ST-3 DNet. ** Optimized parameters: number of residual units, batch size and learning rate. Best values found: 5, 16 and 0.00095. * **STAR*. ** Optimized parameters: number of remaining units, batch size and learning rate. Optimal values found: 2, 16 and 0.0001. * **3D-CLoST. ** Optimized parameters: number of LSTM layers, number of hidden units in each LSTM layer, batch size, and learning rate. Optimal values found: 2, 500, 16, and 0.00076. * **ACFM. ** Optimized parameter: learning rate. Optimal value found: 0.0003. * **STREED-Net. ** Optimized parameters: kernel size, batch size, and learning rate. Optimal values found: 3, 64 and 0.00086.