# Small Fleet, Big Impact: Enhancing Shared Micromobility Efficiency through Minimal Autonomous Vehicle Deployment

arXiv: https://arxiv.org/abs/2510.04271 · HTML: https://mlsanity.com/paper/2510.04271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Towards Autonomous Micromobility through Scalable Urban Simulation](https://mlsanity.com/paper/2505.00690/) (2025)
- [A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems](https://mlsanity.com/paper/1802.04592/) (2018)
- [Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1509.03985/) (2015)
- [MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions](https://mlsanity.com/paper/2408.09768/) (2024)
- [Autonomous Vehicle Public Transportation System: Scheduling and Admission Control](https://mlsanity.com/paper/1502.07242/) (2015)
- [Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing](https://mlsanity.com/paper/2406.14054/) (2024)
- [REALISM: A Regulatory Framework for Coordinated Scheduling in Multi-Operator Shared Micromobility Services](https://mlsanity.com/paper/2508.10166/) (2025)
- [Estimating the potential for shared autonomous scooters](https://mlsanity.com/paper/1909.03679/) (2019)

## Built on by (most cited first, 1 of 1)

- [Bridging Policy and Real-World Dynamics: LLM-Augmented Rebalancing for Shared Micromobility Systems](https://mlsanity.com/paper/2603.00176/) (2026)

## Abstract (arXiv)

Shared micromobility systems, such as electric scooters and bikes, have gained widespread popularity as sustainable alternatives to traditional transportation modes. However, these systems face persistent challenges due to spatio-temporal demand fluctuations, often resulting in a mismatch between vehicle supply and user demand. Existing shared micromobility vehicle scheduling methods typically redistribute vehicles once or twice per day, which makes them vulnerable to performance degradation under atypical conditions. In this work, we design to augment existing micromobility scheduling methods by integrating a small number of autonomous shared micromobility vehicles (ASMVs), which possess self-rebalancing capabilities to dynamically adapt to real-time demand. Specifically, we introduce SMART, a hierarchical reinforcement learning framework that jointly optimizes high-level initial deployment and low-level real-time rebalancing for ASMVs. We evaluate our framework based on real-world e-scooter usage data from Chicago. Our experiment results show that our framework is highly effective and possesses strong generalization capability, allowing it to seamlessly integrate with existing vehicle scheduling methods and significantly enhance overall micromobility service performance.