mlsanityData to 14 Oct 2026

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

arXiv 1404.4391 ↗First posted 16 Apr 2014cs.RO cs.MAcited by 25

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 8; 2022: 1; 2023: 9; 2024: 1; 2025: 2; 2026: 4.

25 papers.

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  1. 2021A Deep Value-network Based Approach for Multi-Driver Order Dispatchingcited by 45
  2. 2021Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systemscited by 28
  3. 2022Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systemscited by 18
  4. 2023Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Controlcited by 14
  5. 2023Convergence Rates for Localized Actor-Critic in Networked Markov Potential Gamescited by 9
  6. 2024Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scalecited by 8
  7. 2021A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problemcited by 6
  8. 2021Pattern Transfer Learning for Reinforcement Learning in Order Dispatchingcited by 4
  9. 2021Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learningcited by 4
  10. 2023Safe Model-Based Multi-Agent Mean-Field Reinforcement Learningcited by 4
  11. 2021Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Networkcited by 3
  12. 2023Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing Systemcited by 3
13 more
  1. 2025Robo-taxi Fleet Coordination at Scale via Reinforcement Learningcited by 3
  2. 2023Scalable Multi-Agent Reinforcement Learning with General Utilitiescited by 2
  3. 2023Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learningcited by 2
  4. 2021DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioningcited by 1
  5. 2023Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Ridesharecited by 1
  6. 2023Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)cited by 1
  7. 2021H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatchcited by 0
  8. 2023Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systemscited by 0
  9. 2025Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approachcited by 0
  10. 2026Learning to Dial-a-Ride: A Deep Graph Reinforcement Learning Approach to the Electric Dial-a-Ride Problemcited by 0
  11. 2026Competitive Multi-Operator Reinforcement Learning for Joint Pricing and Fleet Rebalancing in AMoD Systemscited by 0
  12. 2026Regime-Calibrated Fleet Repositioning with a Spatial Queue-Regret Decompositioncited by 0
  13. 2026MobiWave: Dispatch-Oriented Graph Wavelets and Drift-Guided Selective Optimization for Autonomous Fleet Rebalancingcited by 0