## 2 Related Work  
Numerous studies are investigating generative models based on machine learning approaches in improving data analysis generated by MoD services. One of the most used generative models for generating synthetic or augmenting existing MoD-based data samples is based on the VAE framework. The study [13] uses a VAE-based framework to generate trajectories of taxi trips and compare them with the dataset generated by Floating Car Data (FCD). The resulting reconstructed synthetic data are employed to compute the traffic flows and geographic distribution of parked cars. Additionally, this study investigates the impact of synthetic data on learning performance by changing the number of latent space variables. It does not introduce mixed datasets with generated synthetic and real-world data. The more advanced VAE framework for generating OD matrices of crowd flows is investigated in study [14]. It introduces a Variational Multi-modal Recurrent Graph Auto-Encoder (VMR-GAE) which models the uncertainty-awareness for OD matrix interpolation by formulating the problem as semi-supervised learning on directed graphs. The aforementioned study proves that it is possible to generate an OD matrix that models mobility-based traffic flows using the VAE-inspired framework.  
Furthermore, the additional modification of standard AEs in the form of the Context Augmented Graph Autoencoder (Con-GAE) model is used in study [15]. The proposed model enables leveraging the graph embedding and context embedding techniques to capture the spatial and temporal patterns in sparse and high-dimensional various MoD data and to capture anomalies. Thus, it provides additional proof that it is possible to capture spatiotemporal patterns in MoD flows formatted as the OD matrices.  
In the study [16], the VAE is used for data augmentation to reproduce crash data in highly imbalanced datasets. Thus, this approach is used to increase the sample number of crash data compared to non-crash data. This augmentation of the training dataset has improved the separation process between crash and non-crash data.  
The Generative Adversarial Networks (GAN) represent another generative framework based on the machine learning approach. The GANs are based on the generator which creates new outputs and the discriminator which asses them. Thus, the aforementioned generative framework is based on a supervised learning approach that uses two loss functions for each of their counterparts. The VAE-based frameworks are based only on one loss function implemented for an unsupervised learning approach. Thus, they are easier to train than GANs. In study [17], the GAN approach predicts mobility demand for taxi-based services. The learning dataset is based on higher resolution since it combines spatiotemporal taxi-GPS data of _pick-up_ and _drop-off_ locations with taximeter records. Thus, the GAN approach provides better accuracy in capturing mobility patterns with high-resolution data when its learning framework is based on a Long-Short Term Memory (LSTM) neural network. A similar GAN-based approach for taxi service prediction is used in study [18], where the previous study is augmented by using multi-source data such as weather forecasting and location point-of-interest.  
In summary, the current approaches that are used for the augmentation of datasets based on traffic demand investigate only synthetic data generated by VAE. The second set of studies uses the generative model to detect anomalies in real-world datasets or reconstruct missing elements of existing samples based on traffic demand. The study which is not related to the domain of traffic demand investigates the improvement of crash prediction by using the combination of synthetic and real-world samples in the highly imbalanced dataset. Thus, it only adds the synthetic samples to the category with fewer samples. The proposed research uses a similar approach to a balanced dataset based on traffic demand with different percentages of synthetic samples in each category.