Customer-Centric Dynamic Pricing for Free-Floating Vehicle Sharing Systems
针对自由浮动式车辆共享系统（如共享单车/汽车）中车辆分布不均的问题，提出一种以客户为中心的动态定价方法，通过实时考虑客户位置和步行距离来优化价格，并采用近似动态规划求解，实验表明利润可提升高达8%。
Free-floating vehicle sharing systems such as car or bike sharing systems offer customers the flexibility to pick up and drop off vehicles at any location within the business area and, thus, have become a popular type of urban mobility. However, this flexibility has the drawback that vehicles tend to accumulate at locations with low demand. To counter these imbalances, pricing has proven to be an effective and cost-efficient means. The fact that customers use mobile applications, combined with the fact that providers know the exact location of each vehicle in real-time, provides new opportunities for dynamic pricing. In this context of modern vehicle sharing systems, we develop a profit-maximizing dynamic pricing approach that is built on adopting the concept of customer-centricity. Customer-centric dynamic pricing here means that, whenever a customer opens the provider’s mobile application to rent a vehicle, the price optimization incorporates the customer’s location as well as disaggregated choice behavior to precisely capture the effect of price and walking distance to the available vehicles on the customer’s probability for choosing a vehicle. Two other features characterize the approach. It is origin-based, that is, prices are differentiated by location and time of rental start, which reflects the real-world situation where the rental destination is usually unknown. Further, the approach is anticipative, using a stochastic dynamic program to foresee the effect of current decisions on future vehicle locations, rentals, and profits. We propose an approximate dynamic programming-based solution approach with nonparametric value function approximation. It allows direct application in practice, because historical data can readily be used and main parameters can be precomputed such that the online pricing problem becomes tractable. Extensive numerical studies, including a case study based on Share Now data, demonstrate that our approach increases profits by up to 8% compared with existing approaches from the literature. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2021.0524 .