Human Preference-aware Rebalancing and Charging for Shared Electric Micromobility Vehicles

Shared electric micromobility has surged to a popular model of urban transportation due to its efficiency in short-distance trips and environmentally friendly characteristics compared to traditional automobiles. However, managing thousands of shared electric micromobility vehicles including rebalancing and charging to meet users’ travel demands still has been a challenge. Existing methods generally ignore human preferences in vehicle selection and assume all nearby vehicles have an equal chance of being selected, which is unrealistic based on our findings. To address this problem, we design PERCEIVE, a human preference-aware rebalancing and charging framework for shared electric micromobility vehicles. Specifically, we model human preferences in vehicle selection based on vehicle usage history and current status (e.g., energy level) and incorporate the vehicle selection model into a robust adversarial reinforcement learning framework. We further utilize conformal prediction to quantify human preference uncertainty and fuse it with the reinforcement learning framework. We evaluate our framework using two months of real-world electric micromobility operation data in a city. Experimental results show that our method achieves a performance gain of at least 4.02% in the net revenue and offers more robust performance in worst-case scenarios compared to state-of-the-art baselines.