采购平台的提前预订和供应管理
Book-Ahead & Supply Management for Ridesourcing Platforms
摘要：
乘车平台最近推出了“安排乘车”服务，乘客可以在出行前预订（提前预订）乘车。预订为平台提供了精确的信息，描述了预期未来行程的开始时间和位置;反过来，平台可以使用这些信息来调整驾驶员供应的可用性和空间分布。在这篇文章中，我们提出了一个框架建模/分析保留时变随机乘车系统。我们认为，司机供应分布在一个网络的地理区域和预订提前乘车到达时间优先于非预约乘车。首先，我们提出了一个状态相关的准入控制政策，分配司机乘客，这一政策确保到达时间的服务要求将达到提前预订乘坐。第二，鉴于每个地区的准入控制政策和预订信息，我们预测的“目标”数量的驱动程序，需要（在未来），以概率保证到达时间的服务要求随机非保留的游乐设施。第三，我们提出了一个反应调度/再平衡机制，确定调整驱动程序供应，需要保持跨区域的目标。对于特定的到达时间服务质量，使用曼哈顿Lyft乘车数据的模拟结果显示了空闲司机的数量如何随着预订提前乘车的比例而减少。我们还观察到，非平稳的需求（乘车请求）率随时间的变化显着;这种快速变化进一步说明，时间依赖性模型需要乘车系统的运营分析。
英文摘要：
Ridesourcing platforms recently introduced the ``schedule a ride'' service where passengers may reserve (book-ahead) a ride in advance of their trip. Reservations give platforms precise information that describes the start time and location of anticipated future trips; in turn, platforms can use this information to adjust the availability and spatial distribution of the driver supply. In this article, we propose a framework for modeling/analyzing reservations in time-varying stochastic ridesourcing systems. We consider that the driver supply is distributed over a network of geographic regions and that book-ahead rides have reach time priority over non-reserved rides. First, we propose a state-dependent admission control policy that assigns drivers to passengers; this policy ensures that the reach time service requirement would be attained for book-ahead rides. Second, given the admission control policy and reservations information in each region, we predict the ``target" number of drivers that is required (in the future) to probabilistically guarantee the reach time service requirement for stochastic non-reserved rides. Third, we propose a reactive dispatching/rebalancing mechanism that determines the adjustments to the driver supply that are needed to maintain the targets across regions. For a specific reach time quality of service, simulation results using data from Lyft rides in Manhattan exhibit how the number of idle drivers decreases with the fraction of book-ahead rides. We also observe that the non-stationary demand (ride request) rate varies significantly across time; this rapid variation further illustrates that time-dependent models are needed for operational analysis of ridesourcing systems.
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