## 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{2-7}  & **RMSE** & **MAE** & **Recall** & **Precision** & **F1 Score** \\ \hline \multicolumn{2}{c}{**Initial Training Dataset**} & **0.142** & **0.027** & **0.962** & **0.963** & **0.963** \\ \hline \multicolumn{2}{c}{+12\% synthetic samples for each cluster} & 0.151 & 0.026 & 0.962 & 0.962 & 0.962 \\ \hline \multicolumn{2}{c}{+11\% synthetic samples for each cluster} & 0.145 & 0.023 & 0.964 & 0.964 & 0.964 \\ \hline \multicolumn{2}{c}{+10\% synthetic samples for each cluster} & 0.136 & 0.022 & 0.968 & 0.968 & 0.967 \\ \hline \multicolumn{2}{c}{+9\% synthetic samples for each cluster} & 0.135 & 0.024 & 0.968 & 0.968 & 0.967 \\ \hline \multicolumn{2}{c}{+8\% synthetic samples for each cluster} & 0.132 & 0.020 & 0.970 & 0.970 & 0.970 \\ \cline{2-7} \multicolumn{2}{c}{+7\% synthetic samples for each cluster} & 0.137 & 0.021 & 0.968 & 0.961 & 0.968 \\ \cline{2-7} \multicolumn{2}{c}{+5\% synthetic samples for each cluster} & 0.133 & 0.023 & 0.967 & 0.967 & 0.967 \\ \hline \multicolumn{2}{c}{+3\% synthetic samples for each cluster} & 0.135 & 0.022 & 0.970 & 0.970 & 0.970 \\ \hline \multicolumn{2}{c}{+10\% synthetic samples} & 0.139 & 0.024 & 0.965 & 0.966 & 0.966 \\ \cline{2-7} \multicolumn{2}{c}{only for morning cluster} & 0.134 & 0.022 & 0.970 & 0.970 & 0.970 \\ \hline \multicolumn{2}{c}{+10\% synthetic samples} & 0.148 & 0.031 & 0.962 & 0.963 & 0.963 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance using trained shallow ANN models based on FC 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. the synthetic artefacts are less prominent and less present since the VAE framework had more training samples from which to learn their baseline features. The high accuracy in learning baseline features within a particular cluster is essential for generating valid synthetic samples. Table 7 presents the classification performance results of a trained ANN model based on 2-dimensional convolution layers. It is possible to conclude that an optimal percentage of synthetic samples is below 10% in the initial training dataset. Furthermore, augmentation of the evening cluster with 10% of synthetic samples induces lower prediction results. Those results can be explained by a lower number of training samples within this cluster. The best classification accuracy is achieved with the models trained on an initial training dataset augmented by 8% and 3% with synthetic samples for each cluster. It is necessary to emphasise that in 2-dimensional image-like inputs, artefacts are spatially localised at specific places in synthetic samples. Those spatial artefacts in synthetic samples are generated due to a low number of training samples or an increased number of outliers in that spatial section. Usually, they are defined by pairs of _drop-off,_ and _pick-up_ zones with the highest mobility frequencies where different anomalies can happen for each cluster. The filters in 2-dimensional convolution layers can more accurately detect spatial features of artefacts in synthetic samples through learning. Those learned features can be fitted into the non-linear functions for clusters without negative effects on final classification results. This effect is most prominent in the case of a smaller percentage of synthetic samples. Thus, this introduces fewer artefacts in the combined training dataset. Consequentially, learned non-linear functions for each cluster are less prone to distortion caused by fewer artefacts. All other parts of synthetic samples without artefacts improve the fitting process. This improves the classification accuracy of those models. The increased percentage of synthetic samples implies a higher number of spatial artefacts