A user-based adaptive joint relocation model combining electric car-sharing and bicycle-sharing

This paper presents a user incentive-based adaptive joint relocation model, combining electric car-sharing and bicycle-sharing to optimize the spatiotemporal distribution of shared electric vehicles and increase the use of car-sharing. On the one hand, the optimization model takes subsidy cost and user satisfaction into consideration. On the other hand, multiple dynamic constraints, including the state of charge of electric vehicles, cycling distance, station status, the historical trend of user orders, are considered. Then, the Genetic Algorithm is adapted to solve this joint relocation model; the optimal subsidy price for users, initial vehicle configuration and multi-time-period dynamic thresholds in each station and vehicle relocation scheme are obtained. Finally, a well-known electric vehicle-sharing company is chosen as a case study. The results show that the relocation cost of joint relocation is reduced by around 70% compared with traditional staff relocation, and user satisfaction can be enhanced.