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

2026-02-26

AI Summary

This work addresses the challenge of rebalancing shared micromobility systems under sudden disruptions—such as demand surges, vehicle failures, or policy interventions—where conventional strategies struggle to balance operational efficiency during normal conditions with adaptability during anomalies. To this end, the authors propose AMPLIFY, a novel framework that integrates large language models (LLMs) into the rebalancing task for the first time. By leveraging an LLM’s adaptive reasoning and self-reflection capabilities alongside a reinforcement learning baseline policy, demand forecasts, and real-time system context, AMPLIFY dynamically adjusts vehicle redistribution plans. Experiments on real-world electric scooter data from Chicago demonstrate that the approach significantly improves both service demand fulfillment and system revenue compared to traditional methods, while maintaining strong performance under normal operations and exhibiting robust responsiveness to unforeseen disturbances.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Mechanism Design
• Machine Learning: Scalability of ML Systems

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Shared micromobility services such as e-scooters and bikes have become an integral part of urban transportation, yet their efficiency critically depends on effective vehicle rebalancing. Existing methods either optimize for average demand patterns or employ robust optimization and reinforcement learning to handle predefined uncertainties. However, these approaches overlook emergent events (e.g., demand surges, vehicle outages, regulatory interventions) or sacrifice performance in normal conditions. We introduce AMPLIFY, an LLM-augmented policy adaptation framework for shared micromobility rebalancing. The framework combines a baseline rebalancing module with an LLM-based adaptation module that adjusts strategies in real time under emergent scenarios. The adaptation module ingests system context, demand predictions, and baseline strategies, and refines adjustments through self-reflection. Evaluations on real-world e-scooter data from Chicago show that our approach improves demand satisfaction and system revenue compared to baseline policies, highlighting the potential of LLM-driven adaptation as a flexible solution for managing uncertainty in micromobility systems.

Problem

• shared micromobility
• vehicle rebalancing
• emergent events
• uncertainty
• demand surges

Innovation

• LLM-augmented policy
• real-time adaptation
• shared micromobility
• rebalancing optimization
• emergent event handling