Real-time unmet demand-driven relocation policy to improve service capacity of shared E-mopeds
Journal
Journal of Urban Management
ISSN
2226-5856
Date Issued
2025
Author(s)
Abstract
The growing popularity of shared electric moped scooters (E-mopeds) as a sustainable and convenient transport option faces a fundamental challenge: demand often exceeds supply, necessitating efficient real-time relocation strategies. To address this, we propose an unmet demand-driven relocation policy to improve service efficiency and E-moped utilization. This policy leverages recurrent and diffusion convolutional graph neural networks alongside linear programming. Regional E-moped relocation is suggested to be triggered when its demand falls below a threshold parameter. We demonstrate that the unmet demand strategy consistently outperforms the traditional pick-up method. At a threshold of 3, the unmet demand approach results in 286 relocated E-mopeds compared to 100 using the pick-up strategy, emphasizing the importance of our study. These findings assist operators in implementing more efficient strategies and inform policymakers in refining the maximum fleet sizes and parking space allocation.
Subjects
Graph neural network
Real-time relocation
Shared electric moped scooter service
Spatio-temporal demand prediction
Unmet demand-driven policy
SDGs
Publisher
Elsevier BV
Type
journal article