## 1 Introduction  
Mobility-on-demand (MoD) is one of the most used approaches for enabling personalised commuting services in dense urban regions. The backbone of this mobility approach is a ride-hailing system enabled by a central call centre that serves as a humanly controlled dispatching service for taxi or privately operated rides. Currently, these dispatching processes are completely or partially automated by various web-oriented platforms easily accessible by end-users which are travellers. Automated dispatching of taxi or private car services through web platforms for end-users reduces the cost of owning and buying a personal vehicle or using overcrowded public transport systems. This can be even more prominent in dense urban regions heavily affected by congestion during peak hours. Simultaneously, it enables a personalised selection of destinations at the desired trip time window which depends upon their current location. This trip time window is in a temporal context defined by the moment of online-based ordering or hailing a taxi service and the end of each trip defined by arriving at the desired destination. Their current location is considered as _pick-up_ place while their destination is denoted by _drop-off_ location. Thus, those services have become very popular, especially in dense urban regions such as New York City. It is found that MoD-oriented companies have increased their ride numbers by over 1000% between 2012 and 2019 [1]. The need to develop more comprehensive data-driven dispatching systems for taxi fleets is caused by increased demand for personalised MoD-based services. More comprehensive dispatching decisions for the MoD enabling services are based on a concept known as Demand Responsive Transit (DRT) [2; 3]. This concept is based on proactive-oriented approaches such as travelling demand predictions, optimised routing and accurate traffic anomaly detection (incidents, congestions, etc.) in mobility patterns. Nowadays, numerous studies tackle those problems for different MoD-based services. Most of them are based on various machine learning approaches [4; 5; 6]. Machine learning is the most used data-driven approach for the non-linear approximation of behavioural patterns in stochastic systems such as traffic systems [7; 8]. Thus, sufficiently large, diverse, comprehensive, and consistent training datasets are essential for stable learning convergence with a low probability of early _overfitting_. The real-life datasets gathered from complex stochastic systems such as demand for MoD-based services can induce difficulties in achieving a satisfied diversity and number of use cases for training datasets. Thus, this paper proposes a framework which creates and introduces synthetic samples to the existing training datasets based on real-world MoD-based data. This should be performed without reducing training dataset consistency. Reduced consistency of training dataset leads to lower accuracy of the models based on which they are trained [9; 10]. The MoD-based data are presented as the Origin-Destination (OD) matrices. Thus, the _pick-up_ locations denote origins while destinations of passengers represent their _drop-off_ locations (zones). Autoencoders (AE) in this study are used as the dimensionality reduction method, which encodes each OD-matrix in 2-dimensional latent space. This number of dimensions for latent space representation is chosen for the convenient visualisation of results in a _Cartesian_ coordinated system. Furthermore, the decoder part of the AE framework can be used to reconstruct a multi-dimensional OD matrix from a 2-dimensional latent space. The drawback of classical AE is poor performance in generating OD matrices which are not present in the existing training dataset. Thus, they are not convenient for generating additional samples in the training dataset which differ from existing ones. This drawback is tackled by introducing the Variational Autoencoders (VAE).