Ride-sharing is becoming one of the most important transportation modes in many cities due to its convenience and accessibility. But it is important to assess the impact of network on the ride-sharing performance and vice versa. This paper proposes a complete framework to represent the ride-sharing system functioning. We address the problem for a city scale of 25 km2, a total market of 11,235 shareable trips over a 4 hours period with a rolling horizon of 20 minutes. Furthermore, the solutions are assessed using a dynamic trip-based macroscopic simulation to account for congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies about optimal fleet management that usually consider constant and fully deterministic travel time functions. Then we assess the network performance under different levels of ride-sharing.