Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems

TL;DR

This paper introduces a data-driven MPC framework for controlling autonomous mobility-on-demand systems, utilizing demand forecasting to optimize vehicle rebalancing and significantly reduce customer wait times.

Contribution

It presents a novel end-to-end MPC approach that integrates demand prediction and rebalancing for large-scale autonomous vehicle fleets, outperforming existing strategies.

Findings

Reduces mean customer wait time by up to 89.6%.

Scales efficiently for large systems regardless of fleet size.

Outperforms state-of-the-art rebalancing strategies.

Abstract

The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles). We first model the AMoD system using a time-expanded network, and present a formulation that computes the optimal rebalancing strategy (i.e., preemptive repositioning) and the minimum feasible fleet size for a given travel demand. Then, we adapt this formulation to devise a Model Predictive Control (MPC) algorithm that leverages short-term demand forecasts based on historical data to compute rebalancing strategies. We test the end-to-end performance of this controller with a state-of-the-art LSTM neural network to predict customer demand and real customer data from DiDi Chuxing: we show that this approach scales very well for large systems (indeed, the computational complexity of the MPC algorithm does not depend on the number of customers and of vehicles in the system) and outperforms state-of-the-art rebalancing strategies by reducing the mean customer wait time by up to to 89.6%.