In this paper, we study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing work in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost. (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per incoming rider requests should be incorporated, while it is excluded by most existing solutions. We propose an index structure together with a set of pruning rules and an efficient algorithm to include new riders into drivers' existing trip schedule. To answer new rider requests effectively, we propose two algorithms that match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the proposed methods.