We consider a daily-level profit maximization of a shared mobility on-demand (MoD) service with request-level control, and possible government interventions to improve system efficiency. We use discrete choice models to describe traveler behavior, apply the assortment and price optimization framework to model the request-level dynamics, and leverage insights from dynamic programming to develop daily-level optimization problem. We solve this problem by designing parametric rollout policy and utilizing Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to search for optimal parameter.

We evaluate our algorithm with a case study in Langfang, China. We develop a simulation system for both the MoD service operations and the city transportation dynamics, and design scenarios with varying supply size, demand size, congestion level, and fare structure. In this case study, the optimal pricing strategy generates considerably more profit than basic strategies (those without assortment or dynamic pricing) and myopic strategies (dynamic pricing at each request level), but it increases the congestion level and reduces the capacity in the transportation system. We also compare two policy interventions to improve the system efficiency, i.e. congestion based taxation and demand based taxation, and find that the congestion based taxation is more effective.