Learning to price vehicle service with unknown demand

Abstract

It can be profitable for vehicle service providers to set service prices based on users' travel demand on different origin-destination pairs. Prior studies on the spatial pricing of vehicle service rely on the assumption that providers know users' demand. In this paper, we study a monopolistic provider who initially does not know users' demand and needs to learn it over time by observing the users' responses to the service prices. We design a pricing and vehicle supply policy, considering the tradeoff between exploration (i.e., learning the demand) and exploitation (i.e., maximizing the provider's shortterm payoff). Considering that the provider needs to ensure the vehicle flow balance at each location, its pricing and supply decisions for different origin-destination pairs are tightly coupled. This makes it challenging to theoretically analyze the performance of our policy. We analyze the gap between the provider's expected time-average payoffs under our policy and a clairvoyant policy, which makes decisions based on complete information of the demand. We prove that after running our policy for D days, the loss in the expected time-average payoff can be at most O (ln D) 1 2 D − 1 4 , which decays to zero as D approaches infinity. CCS CONCEPTS • Networks → Network economics; • Theory of computation → Online learning algorithms; • Applied computing → Transportation; • Social and professional topics → Pricing and resource allocation.