Minimizing Fleet Size and Improving Vehicle Allocation of Shared Mobility under Future Uncertainty: A Case Study of Bike Sharing

Mingzhuang Hua, Xuewu Chen*, Jingxu Chen, Yu Jiang

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

As a rapidly expanding type of shared mobility, bike sharing is facing severe challenges of bike over-supply and demand fluctuation in many Chinese cities. In this paper, a large-scale method is developed to determine the minimum fleet size under future demand uncertainty, which is applied in a case study with millions of bike sharing trips in Nanjing. The findings show that if future uncertainty is not considered, more than 12% of trip demands may not be satisfied. Nevertheless, the proposed algorithm for minimizing fleet size based on historical trip data is effective in handling future uncertainty. For a bike sharing system, supplying 14.5% of the original fleet could be sufficient to meet 96.8% of trip demands. Meanwhile, the results suggest a unified platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the Coronavirus Disease 2019 (COVID-19) pandemic, this paper proposes a contact delay policy that maintains a suitable usage interval, which results in increased bike amount requirements. These findings provide useful insights for improving resource efficiency and operational services in shared mobility applications.
Original languageEnglish
Article number133434
JournalJournal of Cleaner Production
Volume370
ISSN0959-6526
DOIs
Publication statusPublished - 2022

Keywords

  • Bike sharing
  • Minimum fleet size
  • Uncertainty
  • Integrated service platform
  • COVID-19

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