## 5 Conclusions
This is proven by analysis which conducts dimensionality reduction based on combined datasets with synthetic and real-world MoD-based samples. The synthetic samples are spatially located in the correct clusters defined by real-world MoD-based samples. The second analysis includes different configurations of training datasets with synthetic and real-world MoD-based samples for the classification of clusters. It is shown that the models trained on the configuration with a lower percentage of synthetic samples combined with the initial training dataset can improve the classification accuracy. Those results are achieved for both investigated architectures of ANN models. Thus, this suggests that the proposed Conv2D VAE framework can produce artefacts defined by the intense fluctuation of mobility demand between pairs of _drop-off_, and _pick-up_ zones. Consequently, this can reduce the categorisation accuracy of trained models based on these datasets with a higher percentage of synthetic samples affected by artefacts. Additionally, the study has shown that more training samples within one cluster (afternoon) than others can improve the classification accuracy of the trained model based on exclusive augmentation of this cluster with synthetic samples. This suggests the importance of training dataset size for learning baseline features of a particular cluster. Those learned features represent the foundation based on which the proposed Conv2D VAE framework generates synthetic samples. The final finding of this study is that the investigated ANN model architecture based on 2-dimensional convolution is less prone to localised spatial artefacts within synthetic samples. Those artefacts in the case of MoD-based data are correctly approximated through learning filters within 2-dimensional convolution layers. Thus, if the training dataset is augmented with up to 10% of synthetic samples it is possible to improve the classification accuracy of investigated predictive ANN-based models that are trained on them. Those results imply that augmentation of the training dataset with synthetic samples generated by the proposed Conv2D VAE can improve the accuracy of predictions using learned spatio-temporal patterns for the approximation of stochastic mobility demand processes within dense urban areas. Additionally, in comparison with results achieved by GAN models, it is possible to conclude that reconstruction of synthetic data from a probabilistic framework used by VAE is highly beneficial for stochastic processes such as generating realistic mobility demand. The proposed VAE framework can be used for deeper spatial analysis of commuters' behaviour for strategic planning of dispatching vehicles within MoD services. Future research will address the more complex architecture of VAE models. Those more complex VAE models will be investigated regarding their impact on generating artefacts in synthetic samples. This is especially needed for the investigation of smaller datasets with numerous outliers, such as the evening cluster in the presented MoD study. The conceptualization of the study was conducted by [PERSON], [PERSON] and [PERSON]. The funding acquisition was conducted by [PERSON] The development and design of the methodology was conducted by [PERSON], [PERSON] and [PERSON]. The writing of the original draft and preparation of the paper were conducted by [PERSON] and [PERSON]. All authors contributed to the writing of the paper and the final editing. Visualizations were conducted by [PERSON] Implementation of the computer code and supporting algorithms was conducted by [PERSON] All authors have read and agreed to the published version of the manuscript. This work was partly supported by the University of Zagreb and the Faculty of Transport and Traffic Sciences under the grants \"Real-time passenger information model in traffic safety\". Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical concerns.