# Dynamic vehicle redistribution and online price incentives in shared mobility systems

arXiv: https://arxiv.org/abs/1304.3949 · HTML: https://mlsanity.com/paper/1304.3949/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2013.

## Built on by (most cited first, 4 of 4)

- [A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems](https://mlsanity.com/paper/2209.08230/) (2022)
- [Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2307.16228/) (2023)
- [Smart Recommendations for Renting Bikes in Bike Sharing Systems](https://mlsanity.com/paper/2401.12322/) (2024)
- [Bike3S: A Tool for Bike Sharing Systems Simulation](https://mlsanity.com/paper/2402.16871/) (2024)

## Abstract (arXiv)

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.