Ride-sourcing is a prominent transportation mode because of its cost-effectiveness and convenience. It provides an on-demand mobility platform that acts as a two-sided market by matching riders with drivers. The conventional models of ride-sourcing systems are equilibrium-based, discrete, and suitable for strategic decisions. This steady-state approach is not suitable for operational decision-making where there is noticeable variation in the state of the system, denying the market enough time to balance back into equilibrium. We introduce a dynamic non-equilibrium ride-sourcing model that tracks the time-varying number of riders, vacant ride-sourcing vehicles, and occupied ride-sourcing vehicles. The drivers are modeled as earning-sensitive, independent contractor, and self-scheduling and the riders are considered price- and quality of service-sensitive such that the supply and demand of the ride-sourcing market are endogenously dependent on (i) the fare requested from the riders and the wage paid to the drivers and (ii) the rider’s waiting time and driver’s cruising time. The model enables investigating how the dynamic wage and fare set by the ride-sourcing service provider affect supply, demand, and states of the market such as average waiting and search time especially when drivers can freely choose their work shifts. Furthermore, we propose a controller based on the model predictive control approach to maximize the service provider’s profit by controlling the fare requested from riders and the wage offered to drivers to satisfy a certain quality of market performance. We assess three pricing strategies where the fare and wage are (i) time-varying and unconstrained, (ii) time-varying and constrained so that the fare is higher than the wage such that the instantaneous profit is positive, and (iii) time-invariant and fixed. The proposed model and controller enable the ride-sourcing service provider to offer a wage to the drivers that is higher than the fare requested from the riders. The result demonstrates that this myopic loss can potentially lead to higher overall profit when customer demand (i.e., riders who may opt to use the ride-sourcing system) increases while the supply of ride-sourcing vehicles decreases simultaneously.

骑乘采购由于其成本效益和便利性而成为一种突出的运输方式。它提供了一个按需移动平台，通过使驾驶员和驾驶员相匹配来充当双向市场。乘车采购系统的常规模型是基于平衡的，离散的，并且适合于战略决策。这种稳态方法不适用于系统状态存在明显变化的运营决策，从而剥夺了市场足够的时间来恢复平衡。我们引入了动态的非平衡乘车来源模型，该模型可跟踪随时间变化的乘员，空乘乘车来源和占用乘车来源车辆的数量。这些驱动程序被建模为对收入敏感的独立承包商，以及自我调度和骑手被认为对价格和服务质量敏感，因此骑车外包市场的供求内生地取决于（i）骑手要求的票价以及支付给驾驶员的工资，以及（ii）驾驶员的等待时间和驾驶员的巡航时间。该模型可以研究出行服务供应商设定的动态工资和票价如何影响供应，需求和市场状态，例如平均等待和搜索时间，尤其是当驾驶员可以自由选择工作班次时。此外，我们提出了一种基于模型预测控制方法的控制器，通过控制车手要求的票价和提供给驾驶员的工资来满足一定质量的市场绩效，从而最大化服务提供商的利润。我们评估了三种定价策略，其中票价和工资是（i）时变且不受约束的；（ii）时变且受约束的，以便使票价高于工资，以使瞬时利润为正；以及（iii）时间-不变和固定。所提出的模型和控制器使乘车外包服务提供商能够向驾驶员提供高于驾驶员要求的票价的工资。结果表明，当客户需求（即可能选择使用拼车采购系统的车手）增加而拼车车辆的供应量同时减少时，这种近视损失有可能导致更高的总体利润。（iii）时不变且固定的。所提出的模型和控制器使乘车外包服务提供商能够向驾驶员提供高于驾驶员要求的票价的工资。结果表明，当客户需求（即可能选择使用拼车采购系统的车手）增加而拼车车辆的供应量同时减少时，这种近视损失可能会导致更高的总体利润。（iii）时不变且固定的。所提出的模型和控制器使乘车外包服务提供商能够向驾驶员提供高于驾驶员要求的票价的工资。结果表明，当客户需求（即可能选择使用拼车采购系统的车手）增加而拼车车辆的供应量同时减少时，这种近视损失有可能导致更高的总体利润。