A Model Predictive Control Scheme for Intermodal Autonomous Mobility-on-Demand

This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.