## 1 Introduction
They use a probabilistic field instead of a regularized one to represent the input data in the latent space [11]. The probabilistic field is described by the statistical distribution of the mean and variance for each input sample instead of points in latent space. Consequentially, this enables the differentiation of newly generated samples by VAE framework compared to existing ones in the training dataset. The VAE approach has shown promising results in the study [12] for reconstructing missing data in existing samples within the dataset with road traffic demand. This dataset is generated based on the data from the fixed road sensors implemented on a simple road network. Thus, this study using the VAE-based approach only for reconstructing missing data in existing samples within the dataset dedicated to road traffic demand. Thus, they do not create entirely new samples for augmenting the entire training dataset. The proposed study in this paper extends the previously discussed research by augmenting initial training datasets based on real-world MoD-based data with synthetic samples generated by the proposed VAE framework. Furthermore, augmented training datasets with different percentages of synthetic samples are used for training the various architectures of predictive models based on Artificial Neural Networks (ANNs). Those trained predictive models are evaluated concerning their classification accuracy and learning convergence compared to the model which is trained on the initial dataset generated exclusively by real-world MoD-based data. Current studies do not provide such an analytical comparison. The novel contribution of the proposed VAE approach is in generating new traffic states represented as samples of OD matrices based on learned patterns from real-world data. Additionally, the proposed approach enables to extension initial dataset based on real-world data with any percentage of synthetic samples per each of the three clusters. Thus, it is possible to conduct comprehensive analysis regarding the optimal number of synthetic samples in the initial dataset based on real-world data which improves learning convergence and prediction accuracy. The limitations of the proposed approach are related to the inability to change specific elements or segments of one synthetic sample coded as the OD matrices. Thus, there is no ability to generate specific disturbances in OD matrices to track their effect on training performance. The proposed study uses traditional VAE architectures with embedded attention mechanisms in the encoder model. Furthermore, the domain regarding the latent space of the specific cluster is determined by using a priori dimensionality reduction with an encoder model in trained VAE architecture. The analysis regarding the impact of weekdays and seasonality is not conducted in this study since its primary goal is to provide preliminary investigation of the proposed concept for the specific stochastic dataset such as MoD records. Therefore, the generalizability and robustness of the proposed concept should be verified and investigated by using other use-cases in future research. The paper is organised as follows. Section 2 offers an overview of previous research concerning the methodologies for synthetic argumentation of training datasets in mobility analysis. The following Section 3 describes the proposed methodologies used in this study. It tackles the techniques used for data preparation, VAE framework design, and approach for generating inputs for the VAE decoder from learned latent space. Section 4 presents the results and findings of the proposed study. This section also provides a comprehensive discussion about possible causes that led to the achieved results. The paper ends with a conclusion in its final section.