## 4 Results and Discussion
### Evaluation of All Investigated Dimensionality Reduction Methods for Clustering Based on MoD Data
It is the number of samples in the training dataset wrongly classified that they do not belong to a particular cluster when they should be classified as positive for that cluster. The _F1 Score_ is computed using Equation (7):  
\[F1\;score=\frac{2\;\times Precision\;\times\;Recall}{Precision+Recall}. \tag{7}\]  
The _F1 Score_ is computed as the harmonic mean of the Precision and Recall values. Thus, it favours smaller differences in values between Precision and Recall. It is possible to conclude that the larger differences between Precision and Recall values induce a smaller harmonic mean. The reduction of the harmonic mean has a negative effect on the investigated model classification accuracy. All explained metrics are commonly used for standard classification problems. Table 6 presents the classification performance results achieved by a trained shallow ANN model based on FC layers. The trained models based on training dataset configurations with synthetic samples improve accuracy over the model trained with the initial training dataset in almost all cases. It is possible to conclude that if the number of augmentations of the initial real-world training dataset are more than 11% of synthetic samples for each cluster, it can reduce prediction accuracy. This larger augmentation of the initial training dataset with synthetic samples for each cluster introduces too many synthetic artefacts in the combined dataset. This consequently reduces the accuracy of predictive models trained by over-augmented configurations of the training dataset. In general, it is possible to notice that shallow ANNs based on FC layers respond minimally to synthetic augmentation of MoD-based datasets. Those results can be explained by the limitations in their architecture, which cannot accurately capture spatial patterns between _drop-off_, and _pick-up_ zones. The second trained model, which performs slightly less accurately compared to the model trained by the initial training dataset, uses an augmentation of the initial training dataset with 10% of synthetic samples for the evening cluster. Those results can be explained by less accurately learned features of the evening cluster, since it contains only two hourly subclusters. To increase the prediction accuracy of the model which is trained on a dataset exclusively augmented by synthetic samples which correspond to the evening cluster, it is necessary to conduct several techniques to improve the learning of its general behaviour patterns. Thus, it is necessary to additionally extend the sub-dataset of the evening cluster with real-world samples which correspond to that cluster. This approach can improve the generalisation of the VAE model for the evening cluster. Thus, the VAE can generate synthetic samples which reassemble more generalised evening behavioural patterns. Consequently, this can reduce the number of artefacts in those synthetic samples that are caused due to frequent outliers in smaller datasets, such as evening clusters. Based on current results on the other two clusters with a lower number of outliers, it is recommended that the percentage of synthetic samples for evening clusters remains at 10%. In general, it is possible to conclude that the shallow architecture of ANN models is less reactive to the augmentation of the initial training dataset with synthetic samples since it does not have advanced abilities to capture deeper structures of features within samples. Those models consider samples as highly dimensional inputs. Thus, the high dimensionality of input samples can reduce sensitivity for artefacts represented by individual features introduced by some parts of synthetic samples. They can slightly improve the fitting process during learning only if they are present in a smaller percentage for each cluster. Furthermore, augmentation of the particular cluster with a higher percentage of the synthetic sample can increase classification performance only if this cluster has a larger number of hourly subclusters, such as the afternoon cluster. In the case of larger clusters,