Résumé

<p>In the near future, the incorporation of shared electric automated and connected mobility<br>(SEACM) technologies will significantly transform the landscape of transportation into a sustainable<br>and efficient mobility ecosystem. However, these technological advances raise complex scientific<br>challenges. Problems related to safety, energy efficiency, and route optimization in dynamic urban<br>environments are major issues to be resolved. In addition, the unavailability of realistic and various<br>data of such systems makes their deployment, design, and performance evaluation very challenging.<br>As a result, to avoid the constraints of real data collection, using generated artificial datasets is<br>crucial for simulation to test and validate algorithms and models under various scenarios. These<br>artificial datasets are used for the training of ML (Machine Learning) models, allowing researchers<br>and operators to evaluate performance and predict system behavior under various conditions. To<br>generate artificial datasets, numerous elements such as user behavior, vehicle dynamics, charging<br>infrastructure, and environmental conditions must be considered. In all these elements, symmetry is<br>a core concern; in some cases, asymmetry is more realistic; however, in others, reaching/maintaining<br>as much symmetry as possible is a core requirement. This review paper provides a comprehensive<br>literature survey of the most relevant techniques generating synthetic datasets in the literature, with<br>a particular focus on the shared electric automated and connected mobility context. Furthermore,<br>this paper also investigates central issues of these complex and dynamic systems regarding how<br>artificial datasets could be used in the training of ML models to address the repositioning problem.<br>Hereby, symmetry is undoubtedly a crucial consideration for ML models. In the case of datasets, it is<br>imperative that they accurately emulate the symmetry or asymmetry observed in real-world scenarios<br>to be effectively represented by the generated datasets. Then, this paper investigates the current<br>challenges and limitations of synthetic datasets, such as the reliability of simulations to the real world,<br>and the validation of generative models. Additionally, it explores how ML-based algorithms can<br>be used to optimize vehicle routing, charging infrastructure usage, demand forecasting, and other<br>important operational elements. In conclusion, this paper outlines a series of interesting new research<br>avenues concerning the generation of artificial data for SEACM systems.</p>