Équipe Modèle et Connaissances (MC) : EL GADI Nourelhouda

Thématiques de recherche : My research focuses on developing a methodology for deploying an autonomous, on-demand, and shared mobility service, optimized through intelligent decision-making systems. The primary objective is to optimize the dynamic routing of shared autonomous shuttles by taking into account both user demand and real-time traffic conditions. The proposed approach leverages artificial intelligence tools, such as ontologies and knowledge graphs, to model mobility patterns at multiple scales and to structure territorial data essential for informed decision-making. In parallel, machine learning models are employed to optimize shuttle itineraries, enabling them to adapt dynamically based on fluctuating demand and road network constraints. This methodology offers an innovative solution for managing mobility services in a smart, flexible, and efficient manner, particularly suited to urban and peri-urban environments.

Points forts des activités de recherche : à venir...