Model Predictive Control of Autonomous Mobility-on-Demand Systems

TL;DR

This paper introduces a model predictive control approach for autonomous mobility-on-demand systems, optimizing vehicle routing and scheduling while considering real-world constraints, and demonstrates its effectiveness with real data.

Contribution

It presents a novel discrete-time model for AMoD systems and a Lyapunov-stable MPC algorithm that efficiently coordinates vehicles in real-time.

Findings

MPC outperforms previous control strategies in simulations.

The model integrates real-world constraints like electric vehicle charging.

The algorithm runs in real-time for moderately-sized systems.

Abstract

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.