Driverless cars are on the way. This technology, allowing more accessible, dynamic and intelligent form of Shared Mobility, is expected to revolutionize urban transportation. One of the conceivable mobility services based on driverless cars are shared autonomous vehicles (SAVs). This service could merge taxis, carsharing and ridesharing systems into a singular transportation mode. However, the success and competitiveness of future SAV services depend on its operational models, which are linked intrinsically to the service configuration and fleet specification. On the other hand, any change on operational models will result in a different demand. Using a comprehensive framework of SAV simulation in a multi-modal dynamic demand system with integrated SAV user taste variation, this study evaluates the performance of various SAV fleets and vehicle capacities serving travelers across the Rouen Normandie metropolitan area in France. The impact of ridesharing and rebalancing strategies on service performance is furthermore investigated. Research results suggest that the performance of SAV is strongly correlated to the fleet size and the strategy of individual or shared rides. Further analysis indicates that for the proposed pricing scheme (20% lower for ridesharing scenario), the standard 4-seats car with shared ride remains the best option among all scenarios. The results underline also that enabling vehicle-rebalancing strategies may have an important effect on both user and service related metrics. The estimated SAV average and maximum driven distance prove the importance of vehicle range and charging station deployment.