  This paper considers a combination of intelligent repositioning decisions and
dynamic pricing for the improved operation of shared mobility systems. The
approach is applied to London's Barclays Cycle Hire scheme, which the authors
have simulated based on historical data. Using model-based predictive control
principles, dynamically varying rewards are computed and offered to customers
carrying out journeys. The aim is to encourage them to park bicycles at nearby
under-used stations, thereby reducing the expected cost of repositioning them
using dedicated staff. In parallel, the routes that repositioning staff should
take are periodically recomputed using a model-based heuristic. It is shown
that a trade-off between reward payouts to customers and the cost of hiring
repositioning staff could be made, in order to minimize operating costs for a
given desired service level.
