# Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective

arXiv: https://arxiv.org/abs/1404.4391 · HTML: https://mlsanity.com/paper/1404.4391/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2014.

## Built on by (most cited first, 25 of 25)

- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2104.11434/) (2021)
- [Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2212.07313/) (2022)
- [Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2302.03963/) (2023)
- [Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games](https://mlsanity.com/paper/2303.04865/) (2023)
- [Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale](https://mlsanity.com/paper/2403.00222/) (2024)
- [A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem](https://mlsanity.com/paper/2105.13284/) (2021)
- [Pattern Transfer Learning for Reinforcement Learning in Order Dispatching](https://mlsanity.com/paper/2105.13218/) (2021)
- [Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2109.11692/) (2021)
- [Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning](https://mlsanity.com/paper/2306.17052/) (2023)
- [Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network](https://mlsanity.com/paper/2102.06854/) (2021)
- [Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System](https://mlsanity.com/paper/2401.00093/) (2023)
- [Robo-taxi Fleet Coordination at Scale via Reinforcement Learning](https://mlsanity.com/paper/2504.06125/) (2025)
- [Scalable Multi-Agent Reinforcement Learning with General Utilities](https://mlsanity.com/paper/2302.07938/) (2023)
- [Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning](https://mlsanity.com/paper/2302.14833/) (2023)
- [DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning](https://mlsanity.com/paper/2109.04149/) (2021)
- [Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare](https://mlsanity.com/paper/2307.02637/) (2023)
- [Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)](https://mlsanity.com/paper/2311.01534/) (2023)
- [H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch](https://mlsanity.com/paper/2105.02138/) (2021)
- [Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2309.08880/) (2023)
- [Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2503.24183/) (2025)
- [Learning to Dial-a-Ride: A Deep Graph Reinforcement Learning Approach to the Electric Dial-a-Ride Problem](https://mlsanity.com/paper/2601.22052/) (2026)
- [Competitive Multi-Operator Reinforcement Learning for Joint Pricing and Fleet Rebalancing in AMoD Systems](https://mlsanity.com/paper/2603.05000/) (2026)
- [Regime-Calibrated Fleet Repositioning with a Spatial Queue-Regret Decomposition](https://mlsanity.com/paper/2604.03883/) (2026)
- [MobiWave: Dispatch-Oriented Graph Wavelets and Drift-Guided Selective Optimization for Autonomous Fleet Rebalancing](https://mlsanity.com/paper/2607.24365/) (2026)

## Abstract (arXiv)

In this paper we present and analyze a queueing-theoretical model for autonomous mobility-on-demand (MOD) systems where robotic, self-driving vehicles transport customers within an urban environment and rebalance themselves to ensure acceptable quality of service throughout the entire network. We cast an autonomous MOD system within a closed Jackson network model with passenger loss. It is shown that an optimal rebalancing algorithm minimizing the number of (autonomously) rebalancing vehicles and keeping vehicles availabilities balanced throughout the network can be found by solving a linear program. The theoretical insights are used to design a robust, real-time rebalancing algorithm, which is applied to a case study of New York City. The case study shows that the current taxi demand in Manhattan can be met with about 8,000 robotic vehicles (roughly 60% of the size of the current taxi fleet). Finally, we extend our queueing-theoretical setup to include congestion effects, and we study the impact of autonomously rebalancing vehicles on overall congestion. Collectively, this paper provides a rigorous approach to the problem of system-wide coordination of autonomously driving vehicles, and provides one of the first characterizations of the sustainability benefits of robotic transportation networks.