Hybrid Mechanisms for On-Demand Transport

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 postedprice
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.