Dynamic vehicle redistribution and online price incentives in shared mobility systems

TL;DR

This paper proposes a combined approach of dynamic pricing and intelligent repositioning to optimize shared mobility systems, demonstrated through simulations of London's bike-sharing scheme, balancing customer incentives and operational costs.

Contribution

It introduces a model-based predictive control framework for real-time dynamic pricing and repositioning in shared mobility, integrating customer incentives with operational cost minimization.

Findings

Dynamic rewards influence customer parking behavior effectively.

Repositioning costs can be reduced by optimizing staff routes.

Trade-offs between incentives and operational costs are quantifiable.

Abstract

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.