Fleet sizing and static rebalancing strategies for shared E-scooters: A case study in Indianapolis, USA

Abstract

With the rapid development of shared e-scooters, it is essential to understand their usage patterns for formulating informed e-scooter fleet management policies. This study first analyzes the usage pattern of shared e-scooters in Indianapolis, USA, by mining big e-scooter trip data. The analysis reveals an oversupply of shared e-scooters relative to actual user demand. Thus, a minimum fleet sizing algorithm is proposed to determine the required minimum e-scooter fleet size with the objective of reducing total operation cost, while ensuring demand coverage. Furthermore, three heuristic algorithms are proposed to address the static e-scooter rebalancing problem, focusing on minimizing rebalancing distance cost and rebalancing time. These algorithms consider practical operational constraints, including the number of rebalancing vehicles, their capacity, and the frequency of visits to e-scooter stations by rebalancing vehicles. The proposed algorithms are applied to e-scooter rebalancing scenarios with comparisons between the minimum and actual fleet sizes. The case study results in Indianapolis, USA demonstrate that the rebalancing distance cost with the minimum fleet size is significantly lower than that with the actual fleet size. What’s more, the rebalancing time can be reduced by about 12.34% to 27.80% when using the minimum fleet size. The findings of this study offer valuable policy implications and managerial insights for shared e-scooter operators and policymakers in developing effective e-scooter management strategies.