Real-time unmet demand-driven relocation policy to improve service capacity of shared E-mopeds

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. • Unmet demand-driven relocation policy significantly outperforms pick-up. • Real-time unmet demand prediction assists in practical relocation policies. • Design strategy for inter-regional vehicle redistribution based on unmet demand gaps.