Title: Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems,
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%.