Real-Time Unmet Demand-Driven Relocation Policy to Improve Service Capacity of Shared E-Mopeds

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.

@article{song2025real,
  title={Real-Time Unmet Demand-Driven Relocation Policy to Improve Service Capacity of Shared E-Mopeds},
  author={Song, Jia-Cherng and Hsieh, I-Yun Lisa and Chen, Chuin-Shan},
  journal={Journal of Urban Management},
  volume={14},
  pages={1340-1355},
  year={2025},
  month=apr,
  doi={10.1016/j.jum.2025.04.001}
}