Space cutting-based approach to optimize vehicle ride-sharing in the city of Lomé ; Approche basée sur le découpage de l'espace pour optimiser le covoiturage dans la ville de Lomé

International audience ; Growing urbanization of the population brings its share of challenges, in particular, regarding global climate change. Contemporary cities are plagued by pollution, traffic congestion, lack of parking space and rising travel costs. In this context, ride-sharing emerges as a promising,environmentally friendly, solution. However, its widespread adoption is hampered by often limiting service quality parameters, namely long travel times and waiting times. Thus, the need arises to implement optimization methods for ride-sharing that would minimize these metrics. Several studies have proposed solutions that benefit from the potential of metaheuristics and machine learning, to address different purposes. Some have studied the impact of ride-sharing on cities , while others have developed heuristics for optimizing dynamic ride-sharing, using shareability function and clustering algorithms , multi-agent simulation-based model, etc. In this study, we devise a solution of the ride-sharing problem based on the division of the area of interest (Lomé, Togo) and then the use of a genetic algorithm to optimize vehicle ride-sharing.