TY - JOUR
T1 - Minimizing Fleet Size and Improving Vehicle Allocation of Shared Mobility under Future Uncertainty: A Case Study of Bike Sharing
AU - Hua, Mingzhuang
AU - Chen, Xuewu
AU - Chen, Jingxu
AU - Jiang, Yu
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Bike sharing
KW - Minimum fleet size
KW - Uncertainty
KW - Integrated service platform
KW - COVID-19
U2 - 10.1016/j.jclepro.2022.133434
DO - 10.1016/j.jclepro.2022.133434
M3 - Journal article
SN - 0959-6526
VL - 370
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 133434
ER -