Vehicle Rebalancing

Vehicle rebalancing optimizes the distribution of vehicles (bikes, ride-hailing cars, autonomous electric vehicles) across a service area to meet fluctuating demand and ensure efficient system operation. Current research heavily utilizes reinforcement learning, often employing multi-agent architectures and deep Q-networks, to dynamically adjust vehicle locations in response to real-time data and predicted demand, sometimes incorporating graph neural networks for improved spatial-temporal prediction. This work addresses crucial challenges in urban mobility, improving service accessibility, reducing operational costs, and promoting fairness in service provision across different communities. Furthermore, robust optimization techniques are being explored to account for uncertainties in demand and vehicle availability, particularly relevant for electric vehicle fleets.