With the development of shared mobility (e.g., ride-sourcing systems such as Uber, Lyft and Didi), there has been a growing interest in pricing and empty vehicle relocation to maximize system performance. Although customers exhibit patience during their waiting for available driver, it has been neglected in most studies due to the complexities it introduces. In this work, we develop a provably near-optimal dynamic pricing and empty vehicle relocation mechanism for a ride-sourcing system with limited customer patience. We model the ride-sourcing system as a network of double-ended queues. To derive a near-optimal control policy, we first establish a fluid limit for the network in a large market regime and show that the fluid-based optimal solution provides an upper bound of the performance of the original ride-sourcing system for all dynamic policies. Then, we develop a simple dynamic policy for the original problem based on the fluid solution and show that its performance almost matches the upper bound. Among our results, we answer two open questions raised in the literature: (i) the performance of our policy converges to that of the true optimal value exponentially fast in time when the market size is large; (ii) the customer loss of our proposed policy decreases to zero exponentially fast when market size increases. This talk is based on joint work with M. Abdolmaleki, T. Radvand, and Y. Yin at the University of Michigan.