Hybrid Mechanisms for On-Demand Transport

Market mechanisms are now playing a key role in the allocation and pricing of on-demand transportation services. In practice, most such services use posted-price mechanisms, where both passengers and drivers are offered a journey price which they can accept or reject. However, providers such as Liftago and GrabTaxi have begun to adopt a mechanism whereby auctions are used to price drivers. These latter mechanisms are neither posted-price nor classical double auctions and can instead be considered a hybrid mechanism. In this paper, we describe and study the properties of a novel hybrid on-demand transport mechanism. As these mechanisms require knowledge of passenger demand, we analyze the data-profit tradeoff as well as how the passenger and driver preferences influence mechanism performance. We show that the revenue loss for the provider scales with $\sqrt {n\log n}$ for $n$ passenger requests under a multi-armed bandit learning algorithm with beta-distributed preferences. We also investigate the effect of subsidies on both profit and the number of successful journeys allocated by the mechanism, comparing these with a posted-price mechanism, showing improvements in profit with a comparable number of successful requests.