Dynamic pricing for shared mobility systems based on idle time data
针对共享出行系统，提出一种基于空闲时间数据的动态定价方法，通过在线优化预测未来利润，在数值实验中利润比现有方法提升最高11%。
Abstract In most major cities today, various shared mobility systems such as car or bike sharing exist. Maintaining these systems is challenging, and, thus, public and private providers strive to improve operational performance. An important metric which is regularly recorded and monitored in practice for this purpose is idle time, i.e., the time a vehicle stands unused between two rentals. Usually, it is available for different temporal and spatial granularities. At the same time, dynamic pricing has been shown to be an efficient means for increasing operational performance in shared mobility systems, but data necessary for traditional dynamic pricing approaches, like unconstrained demand, is much less available in practice. Thus, dynamic pricing based on idle time data appears promising and first ideas have been proposed. However, the existing approaches are based either on simple business rules or on myopic optimization. In this work, we develop a novel dynamic pricing approach that determines prices by online optimization and thereby anticipates future profits through the integration of idle time data. The core idea is quantifying the remaining profitable time by using idle times. With regard to application in practice, the developed approach is generic in the sense that different types of readily available historical idle time data can be seamlessly integrated, meaning data of different spatio-temporal granularities. In an extensive numerical study, we demonstrate that the operational performance increases with higher granularity and that the approach with the highest one outperforms current pricing practice by up to 11% in terms of profit.