在共乘场景中，具有相似行程和时间安排的多名乘客一同出行，可降低出行成本、提高车辆上座率和缓解交通拥堵。现有研究忽略了共乘收费标准不统一和司机恶意竞价对乘客共乘体验的影响。在同时考虑费用约束、车辆容量约束和绕路距离约束的的情况下，提出最大化匹配结果公平性的方案，并将共乘的定价与匹配过程建模为一个两阶段的主从博弈。针对上述方案，提出了一个基于K-means++的请求划分算法,以缩小司乘匹配范围，提高匹配效率；在满足所有参与者约束的前提下，设计了基于两阶段主从博弈的迭代算法DPMA，并从理论上证明了其收敛性。在纽约出租车数据集上进行了仿真实验，通过不同的参数设置验证了DPMA的收敛性。与已有的2个算法相比，DPMA在保障司机收益的同时，在公平指数上分别提高了34.03%和24.42%。实验结果表明所设计机制可以有效避免司机间的恶意竞价，且提高了共乘匹配的公平性。

In a ridesharing scenario, multiple passengers with similar itineraries and time schedules take one vehicle to reduce travel cost, increase vehicle occupancy and ease traffic congestion. However, the existing studies ignore the effect of inequitable charging standard and malicious bidding behavior on passengers ridesharing Quality of Experience (QoE). Considering the constraints of cost, vehicle capacity and detour distance , this paper proposes the problem of maximizing the fairness of matching results, and models the process of ridesharing pricing and matching as a two-stage Stackelberg game. Aiming at the above problems, a request division algorithm based on K-means++ is proposed to narrow the match- ing range and improve the matching efficiency. On the premise of satisfying all participant constraints, an iterative algorithm DPMA based on two-stage Stackelberg game is designed and its convergence is proved theoretically. Simulation experiments are carried out on the New York taxi dataset, and the convergence of the algorithm DPMA is verified under different parameter settings. Compared with two existing algorithms, DPMA improves the fairness index by 34.03% and 24.42%, respectively, while ensuring the drivers benefits. The experimental results show that the designed mechanism can effectively avoid malicious bidding among drivers and improve the fairness of ridesharing matching.