  Today, driverless cars, as a new technology that allows a more accessible,
dynamic and intelligent form of Shared Mobility, are expected to revolutionize
urban transportation. One of the conceivable mobility services based on
driverless cars is shared autonomous vehicles (SAVs). This service could merge
cabs, carsharing, and ridesharing systems into a singular transportation mode.
However, the success and competitiveness of future SAV services depend on their
operational models, which are linked intrinsically to the service configuration
and fleet specification. In addition, any change in 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. Also, the impact of ridesharing and rebalancing strategies on
service performance is investigated.Research results suggest that the
performance of SAV is strongly correlated with the fleet size and the strategy
of individual or shared rides. Further analysis indicates that for the pricing
scheme proposed in this study (i.e., 20% lower for ridesharing scenario), the
standard 4-seats car with shared ride remains the best option among all
scenarios. The results also underline 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.
