On-demand ride services reshape urban transportation systems, human mobility, and travelers' mode choice behavior. Compared to the traditional street-hailing taxi, an on-demand ride services platform analyzes ride requests of passengers and coordinates real-time supply and demand with dynamic operational strategies in the ride-sourcing market. To test the impact of dynamic optimization strategies on the ride-sourcing market, this paper proposes a dynamic vacant car-passenger meeting model. In this model, the accumulative arrival rate and departure rate of passengers and vacant cars determine the waiting number of passengers and vacant cars, while the waiting number of passengers and vacant cars in turn influence the meeting rate (which equals to the departure rate of both passengers and vacant cars). The departure rate means the rate at which passengers and vacant cars match up and start a paid trip. Compared with classic equilibrium models, this model can be utilized to characterize the influence of short-term variances and disturbances of current demand and supply (i.e., arrival rates of passengers and vacant cars) on the waiting numbers of passengers and vacant cars. Using the proposed meeting model, we optimize dynamic strategies under two objective functions, i.e., platform revenue maximization, and social welfare maximization, while the driver's profit is guaranteed above a certain level. We also propose an algorithm based on approximate dynamic programming (ADP) to solve the sequential dynamic optimization problem. The results show that our algorithm can effectively improve the objective function of the multi-period problem, compared with the myopic algorithm. A broader range of surge pricing and commission rate and the introduction of incentives are helpful to achieve better optimization results. The dynamic optimization strategies help the on-demand ride services platform efficiently adjust supply and demand resources and achieve specific optimization goals.

按需乘车服务重塑了城市交通系统，人员出行和旅行者的模式选择行为。与传统的街叫出租车相比，按需乘车服务平台可分析乘客的乘车需求，并通过乘车采购市场中的动态运营策略协调实时供求。为了测试动态优化策略对乘车来源市场的影响，本文提出了一种动态的空置乘车人会议模型。在此模型中，乘客和空车的累积到达率和离场率决定了乘客和空车的等待数，而乘客和空车的等待数又影响会议率（等于两者的离场率乘客和空车）。离站率是​​指旅客和空车相匹配并开始有偿旅行的比率。与经典的均衡模型相比，该模型可用于表征短期变化和当前需求和供给的干扰（即，乘客和空车的到达率）对乘客和空车的等待数量的影响。使用提出的会议模型，我们在两个目标功能（平台收入最大化和社会福利最大化）下优化动态策略，同时确保驾驶员的利润在一定水平以上。我们还提出了一种基于近似动态规划（ADP）的算法来解决顺序动态优化问题。结果表明，我们的算法可以有效地改善多周期问题的目标函数，与近视算法相比。更大范围的激增价格和佣金率以及引入激励措施有助于获得更好的优化结果。动态优化策略可帮助按需乘车服务平台有效地调整供需资源并实现特定的优化目标。