# Model Predictive Control of Autonomous Mobility-on-Demand Systems

arXiv: https://arxiv.org/abs/1509.03985 · HTML: https://mlsanity.com/paper/1509.03985/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2015.

## Built on by (most cited first, 9 of 9)

- [Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2104.11434/) (2021)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems](https://mlsanity.com/paper/2209.08230/) (2022)
- [Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2307.16228/) (2023)
- [Robo-taxi Fleet Coordination at Scale via Reinforcement Learning](https://mlsanity.com/paper/2504.06125/) (2025)
- [Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times](https://mlsanity.com/paper/2208.14852/) (2022)
- [Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning](https://mlsanity.com/paper/2502.04399/) (2025)
- [Small Fleet, Big Impact: Enhancing Shared Micromobility Efficiency through Minimal Autonomous Vehicle Deployment](https://mlsanity.com/paper/2510.04271/) (2025)
- [SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing](https://mlsanity.com/paper/2411.18432/) (2024)

## Abstract (arXiv)

In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an urban environment and are coordinated to optimize service throughout the entire network. Specifically, we first propose a novel discrete-time model of an AMoD system and we show that this formulation allows the easy integration of a number of real-world constraints, e.g., electric vehicle charging constraints. Second, leveraging our model, we design a model predictive control algorithm for the optimal coordination of an AMoD system and prove its stability in the sense of Lyapunov. At each optimization step, the vehicle scheduling and routing problem is solved as a mixed integer linear program (MILP) where the decision variables are binary variables representing whether a vehicle will 1) wait at a station, 2) service a customer, or 3) rebalance to another station. Finally, by using real-world data, we show that the MPC algorithm can be run in real-time for moderately-sized systems and outperforms previous control strategies for AMoD systems.