In this talk I will show the research I am carrying out at Stanford University on intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, I will first present a network flow model for intermodal AMoD, capturing the coupling between AMoD and public transit and where the goal is to maximize social welfare. Second, leveraging such a model, I will devise a pricing and tolling scheme that allows to achieve the social optimum under the assumption of a perfect market with selfish agents. Third, I will present a real-world case study for New York City and Berlin. The results will show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation. Finally, on the operational side, I will present an MPC scheme for intermodal routing and some preliminary results we have obtained for New York City.

Bio: Dr. Mauro Salazar is a Postdoctoral Scholar at the Autonomous Systems Lab in the Department of Aeronautics and Astronautics at Stanford University. He received a Ph.D degree in Mechanical Engineering from ETH Zurich in 2019. Dr. Salazar's research is at the interface of control theory and optimization, and is aimed at the development of a comprehensive set of tools for the design, the deployment and the operation of future mobility systems. Specifically, his area of expertise includes optimal control theory, model predictive control, hybrid electric vehicles, and autonomous mobility-on-demand. Dr. Salazar received the Outstanding Bachelor Award and the Excellence Scholarship and Opportunity Award from ETH Zurich. His Master thesis was recognized with the ETH Medal. He was awarded the Best Student Paper award at the 2018 Intelligent Transportation Systems Conference.

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