目前，我国拼车出行交通模式的市场份额相对较低，在缓解交通拥堵、节能减排方面仍有巨大潜力未被充分挖掘。基于奖励的交通需求管理策略可以提高民众拼车出行意愿，但奖励方案的设置与拼车出行的市场渗透率高度相关，不合理的奖励方案容易导致成本增加，甚至项目破产。为进一步激发拼车需求，合理利用交通资源，文中提出一种基于路段的拼车奖励方案优化模型，以拼车为支点，以奖励为杠杆，实现社会出行总成本的降低。其中，上层模型旨在寻找最优拼车奖励方案以最小化社会出行总成本，下层模型为相应奖励方案下的拼车出行车辆和单人驾驶车辆用户均衡流量分配模型。采用嵌套Frank-Wolfe算法的遗传算法求解该模型，并以Sioux Falls路网和Nguyen Dupuis路网为算例对模型的可行性及有效性进行了验证。结果表明：不符合市场渗透率的预算投入会导致社会总成本大幅上涨；在最优拼车奖励方案下，社会出行总成本降低约24.53%，50%的拥堵路段的拥堵得到缓解，出行公平性问题得到缓和。文中提出的模型可为道路管理者设置科学、合理的拼车奖励方案提供理论基础。

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.