## 4 Results and Discussion
### Evaluation of All Investigated Dimensionality Reduction Methods for Clustering Based on MoD Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Training Dataset Configuration**} & \multicolumn{4}{c}{**Prediction Performance Metrics**} \\ \cline{2-6}  & **RMSE** & **MAE** & **Recall** & **Precision** & **F1 Score** \\ \hline
**Initial Training Dataset** & **0.179** & **0.042** & **0.939** & **0.941** & **0.943** \\ \hline +12\% synthetic samples for each cluster & 0.339 & 0.168 & 0.789 & 0.776 & 0.783 \\ \hline +11\% synthetic samples for each cluster & 0.309 & 0.105 & 0.845 & 0.845 & 0.845 \\ \hline +10\% synthetic samples for each cluster & 0.162 & 0.028 & 0.957 & 0.608 & 0.958 \\ \hline +9\% synthetic samples for each cluster & 0.147 & 0.026 & 0.962 & 0.962 & 0.962 \\ \hline +8\% synthetic samples for each cluster & 0.148 & 0.024 & 0.964 & 0.964 & 0.964 \\ \hline +7\% synthetic samples for each cluster & 0.156 & 0.026 & 0.960 & 0.960 & 0.960 \\ \hline +5\% synthetic samples for each cluster & 0.152 & 0.025 & 0.961 & 0.961 & 0.961 \\ \hline +3\% synthetic samples for each cluster & 0.149 & 0.024 & 0.964 & 0.964 & 0.964 \\ \hline +10\% synthetic samples & 0.161 & 0.03 & 0.958 & 0.958 & 0.958 \\ \hline +10\% synthetic samples & 0.163 & 0.029 & 0.957 & 0.957 & 0.957 \\ \hline +10\% synthetic samples & 0.202 & 0.054 & 0.919 & 0.926 & 0.922 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification performance using trained ANN model based on 2-dimensional convolution layers. The individual training of each model is performed using different configurations of training datasets augmented by the proposed VAE model. All models are tested with the same validation datasets generated by real-world MoD-based data. within them in the combined learning dataset. This reduces the stability of the fitting process during the training and consequently classification accuracy. Despite that, all investigated models trained on different configurations of augmentation for each cluster with synthetic samples achieve better classification accuracy than the initial training dataset. This suggests that the proposed Conv2D VAE framework generates other spatial parts of synthetic samples without prominent artefacts. Results suggest that areas of those parts in synthetic samples are much larger than the ones affected by problematic artefacts caused by the intense fluctuations in mobility demand. Thus, they are the main features of synthetic samples that drive the improvement of classification accuracy over the model trained using an initial real-world MoD-based dataset. This is the case since the spatial filters of the Conv2D VAE framework are less prone to adjustments in areas of synthetic samples which are prone to artefacts. This is the case since at the corresponding places in the real-world MoD-based samples, the outliers (approximated as artefacts by Conv2D VAE) can also occur with high probability. For the sake of comparative analysis with the proposed VAE approach, the GAN model is used to generate synthetic samples. The GAN model cannot be used directly for dimensionality reduction using latent space, based on which its decoder creates synthetic samples for specific clusters. Thus, three identical GAN models are created. Each of those three GAN models is individually trained on samples from one of the three investigated clusters. Thus, one GAN model is used to create synthetic samples for a specific cluster upon which it is trained. It is possible to conclude that this approach is not feasible for a larger number of clusters with a lower number of samples. Table 8 presents the classification performance results conducted by a trained 2D-convolution ANN model with synthetic samples generated by GAN models. It is possible to conclude that trained GAN models can improve the prediction accuracy up to the 7% of synthetic samples in the augmented training dataset. This is a lower percentage of synthetic samples in the augmented training dataset, which can induce prediction improvements compared to the proposed VAE approach.