Bridging Policy and Real-World Dynamics: LLM-Augmented Rebalancing for Shared Micromobility Systems

Researchers introduce AMPLIFY, an LLM-augmented framework for optimizing shared micromobility vehicle rebalancing in urban transportation systems. The system combines baseline rebalancing algorithms with real-time AI adaptation to handle emergent events like demand surges and regulatory changes, showing improved performance in Chicago e-scooter data testing.

• →AMPLIFY framework uses LLMs to adapt micromobility rebalancing strategies in real-time for emergent scenarios.
• →Traditional rebalancing methods fail to account for unexpected events like demand surges or regulatory interventions.
• →The system combines baseline optimization with an LLM adaptation module that includes self-reflection capabilities.
• →Real-world testing on Chicago e-scooter data showed improvements in demand satisfaction and system revenue.
• →The approach demonstrates potential for LLM-driven solutions in managing uncertainty in urban transportation systems.