One-way carsharing is an alternative shared-use transportation mode that provides flexible travel accommodations for urban mobility. However, vehicle distributions can be mismatched with demand distributions because users are not required to return to their departure locations. Conventional operator-based vehicle relocation is limited by labor resources, but user-based and demand-controlled approaches can open new avenues for mitigating vehicle imbalance. This paper proposes a method for controlling demand patterns by applying measures of combinatorial monetary incentives and surcharges. A two-level nested logit model is adopted to analyze user decisions regarding the travel process in response to differentiated pricing combinations. A user choice model is aggregated and loaded into a time-space network that reveals the dynamics of the carsharing system. An optimization framework is proposed to determine the incentives and surcharges at different stations and times of day. This paper presents an algorithm for solving the proposed optimization model, as well as an example of parameter calibration and the solving process. Case analysis suggests that the proposed method can increase revenues by 22.5% compared to a scenario without demand control and vehicle relocation policies. Comparisons suggest that the proposed demand-based control policy can achieve higher revenues than operator-based relocation, whereas operator-based relocation could satisfy greater demand.