Équipe Modèle et Connaissances (MC) : EL GADI Nourelhouda

Thématiques de recherche : My research focuses on developing a decision-making methodology for dynamic, optimized, and adaptive routing of autonomous, shared, on-demand shuttles as part of an autonomous, on-demand mobility project in the regions around La Rochelle. The main objective is to develop an intelligent system that allows the autonomous shuttle to choose the best route in each situation, taking into account the physical complexity of the urban environment, unforeseen events, and real-time traffic conditions, while responding to simultaneous user requests. To achieve this goal, I am using symbolic artificial intelligence tools, based on ontologies and knowledge graphs, to model mobility patterns at different scales and structure the territorial data essential for informed decision-making, as well as optimization methods to design algorithms that exploit this semantic information to produce informed and explained decisions.

Points forts des activités de recherche : My research is distinguished by an integrated and innovative approach to dynamic routing of on-demand autonomous shuttles, combining symbolic AI (ontologies, knowledge graphs) and optimization methods. This hybridization makes it possible to transform heterogeneous data into structured and usable knowledge, then integrate it into decision-making algorithms in order to make informed and explainable decisions and improve the choice of the most relevant routes in real-world contexts, taking into account urban constraints, demand dynamics, and traffic hazards.