论文标题
自动启动在需求系统中的联合定价和重新平衡
Joint Pricing and Rebalancing of Autonomous Mobility-on-Demand Systems
论文作者
论文摘要
本文研究了自动启动按需（AMOD）系统的最佳定价和重新平衡政策。我们采用宏观计划的观点来解决利润最大化问题，同时确保系统平衡。我们首先使用动态流体模型来描述系统，以通过定价策略来显示平衡（即负载平衡）的存在和稳定性。然后，我们开发一个优化框架，使我们能够从定价和重新平衡方面找到最佳的政策。我们首先仅通过使用定价策略来最大化利润，然后结合重新平衡，最后考虑是否依次还是共同找到解决方案。我们使用纽约市的实际出租车数据应用于数据驱动的案例研究。根据我们使用的基准测试解决方案，联合问题（即定价和重新平衡）将利润增加7％至40％
This paper studies optimal pricing and rebalancing policies for Autonomous Mobility-on-Demand (AMoD) systems. We take a macroscopic planning perspective to tackle a profit maximization problem while ensuring that the system is load-balanced. We begin by describing the system using a dynamic fluid model to show the existence and stability of an equilibrium (i.e., load balance) through pricing policies. We then develop an optimization framework that allows us to find optimal policies in terms of pricing and rebalancing. We first maximize profit by only using pricing policies, then incorporate rebalancing, and finally we consider whether the solution is found sequentially or jointly. We apply each approach on a data-driven case study using real taxi data from New York City. Depending on which benchmarking solution we use, the joint problem (i.e., pricing and rebalancing) increases profits by 7% to 40%
