# Analysis and Control of Autonomous Mobility-on-Demand Systems

arXiv: https://arxiv.org/abs/2106.14827 · HTML: https://mlsanity.com/paper/2106.14827/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2021.

## Built on by (most cited first, 6 of 6)

- [Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2302.03963/) (2023)
- [Offline Hierarchical Reinforcement Learning via Inverse Optimization](https://mlsanity.com/paper/2410.07933/) (2024)
- [Soft-Radial Projection for Constrained End-to-End Learning](https://mlsanity.com/paper/2602.03461/) (2026)
- [Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning](https://mlsanity.com/paper/2311.05780/) (2023)
- [Pro-Routing: Proactive Routing of Autonomous Multi-Capacity Robots for Pickup-and-Delivery Tasks](https://mlsanity.com/paper/2503.24325/) (2025)
- [GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding](https://mlsanity.com/paper/2512.03194/) (2025)

## Abstract (arXiv)

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.