Our research focuses on the design and optimization of service models in on-demand transportation systems, leveraging both steady-state analytical models and state-dependent dynamic programming techniques. We aim to capture and address the unique characteristics of these systems, including the stochastic nature of demand, the dynamic interactions between passengers and service providers, and the operational constraints faced by fleet operators. By considering the complex interplay among multiple stakeholders—such as passengers, drivers, platform operators, and regulators—we develop comprehensive frameworks to assess and improve system performance.