Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning

1 code implementation • 9 Nov 2023 • Aaryan Singhal, Daniele Gammelli, Justin Luke, Karthik Gopalakrishnan, Dominik Helmreich, Marco Pavone

Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available vehicles to ride requests, rebalancing idle vehicles to areas of high demand, and charging vehicles to ensure sufficient range.

Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting

no code implementations • 1 Jul 2021 • Justin Luke, Mauro Salazar, Ram Rajagopal, Marco Pavone

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems.

On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow Approach

1 code implementation • 1 May 2019 • Alvaro Estandia, Maximilian Schiffer, Federico Rossi, Justin Luke, Emre Can Kara, Ram Rajagopal, Marco Pavone

Specifically, we extend previous results on an optimization-based modeling approach for electric AMoD systems to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints.

Self-Driving Cars