Minimizing Fleet Size and Improving Bike Allocation of Bike Sharing under Future Uncertainty

TL;DR

This paper presents a large-scale method to optimize fleet size and bike allocation in bike sharing systems under future uncertainty, demonstrating significant reductions in fleet size and improved demand satisfaction.

Contribution

It introduces a novel algorithm for minimizing fleet size under uncertainty and proposes an integrated platform approach and social distancing policies for enhanced efficiency.

Findings

Reducing fleet size by 14.5% still meets 96.8% of demand.

Integrated platform reduces total fleet size by 44.6%.

Social distancing policies maintain safety and service quality.

Abstract

As a rapidly expanding service, bike sharing is facing severe problems of bike over-supply and demand fluctuation in many Chinese cities. This study develops a large-scale method to determine the minimum fleet size under uncertainty, based on the bike sharing data of millions of trips in Nanjing. It is found that the algorithm of minimizing fleet size under the incomplete-information scenario is effective in handling future uncertainty. For a dockless bike sharing system, supplying 14.5% of the original fleet could meet 96.8% of trip demands. Meanwhile, the results suggest that providing a integrated service platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the COVID-19 pandemic, this study proposes a social distancing policy that maintains a suitable usage interval. These findings provide useful insights for improving the resource efficiency and operational service of bike sharing and shared mobility.