At present, the market share of ridesharing in China is relatively low, and there is still huge potential to be fully tapped in alleviating traffic congestion and reducing energy consumption and emissions. Incentive-based traffic demand management strategies can promote people’s willingness of ridesharing, but the design of incentive schemes is highly correlated with the market penetration of ridesharing. An unreasonable incentive scheme may easily lead to increased cost or even project failure. In order to further stimulate the potential ridesharing demand and make reasonable use of transportation resources, a road segment-based incentive optimization model for ridesharing is proposed, which uses ridesharing as the fulcrum and rewards as the lever to reduce the total social travel cost. The upper model of the proposed model aims to find the optimal incentive scheme to minimize the total social travel cost, and the lower model is a user equilibrium allocation model of ridesharing vehicles and single-driver vehicles under the incentive scheme. The iterative algorithm combining the genetic algorithm and the Frank-Wolfe algorithm is used to solve the upper and lower models, respectively, and the feasibility and effectiveness of the model are verified by using the Sioux Falls and Nguyen Dupuis transportation networks as numerical examples. The results show that budget investments that do not meet market penetrations may result in a significant increase in total social travel costs; and that, under the optimal incentive scheme, the total social travel cost is reduced by about 24.53%, the congestion of 50% of congested links is alleviated, and the fairness issue in traffic demand management is alleviated to a certain extent. Thus, there comes to the conclusion that the proposed model can provide theoretical support for managers to set up scientific and reasonable incentive-based ridesharing schemes.