Scalable Model Predictive Control for Autonomous Mobility-on-Demand Systems

Technological advances in self-driving vehicles will soon enable the implementation of large-scale mobility-on-demand (MoD) systems. The efficient management of fleets of vehicles remains a key challenge, in particular to achieve a demand-aligned distribution of available vehicles, commonly referred to as rebalancing. In this article, we present a discrete-time model of an autonomous MoD system, in which unit capacity self-driving vehicles serve transportation requests consisting of a (time, origin, destination) tuple on a directed graph. Time delays in the discrete-time model are approximated as first-order lag elements yielding a sparse model suitable for model predictive control (MPC). The well-posedness of the model is demonstrated, and a characterization of its equilibrium points is given. Furthermore, we show the stabilizability of the model and propose an MPC scheme that, due to the sparsity of the model, can be applied even to large-scale cities. We verify the performance of the scheme in a multiagent transport simulation and demonstrate that service levels outperform those of the existing rebalancing schemes for identical fleet sizes.