Data-driven analysis of optimal repositioning policy in bike sharing systems

Abstract

In this study, we analyze the impact of optimal repositioning policy on the efficiency of bike sharing systems under several performance measures. We first develop a multi-period network flow model to find the optimal repositioning decisions which consist of the origin, destination, and the time of the repositioning that maximize the total profit of a sharing system. We further extend this model to consider demand uncertainty under a stochastic setting. The proposed model is then implemented on the real-world bike-sharing data of New York City, Toronto, and Vancouver. After finding the optimal repositioning policies, we analyze the effect of repositioning on the profit, fulfilled demand, number of required vehicles, and utilization rates of the vehicles. Through computational experiments, we show that repositioning significantly increases the efficiency of a sharing system under these performance measures. In particular, our analyses show that 32% more profit can be obtained and up to 41% more demand can be satisfied with repositioning. Moreover, it is possible to reduce the required fleet size by up to 61% and increase the average utilization rate of the vehicles by up to 21% by employing repositioning.

Keywords:

• Repositioning
• rebalancing
• bike-sharing
• optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All the data that is used in this study is publicly available.

Additional information

Funding

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) with [grant RGPIN-2022-03523].