Market mechanisms are now playing a key role in allocating and pricing on-demand transportion 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 passenger and driver preferences influence mechanism performance. We show that the revenue loss for the provider scales with √ 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.