Paparella, Fabio (Eindhoven University of Technology), Lucas, Clim (Eindhoven University of Technology), Cenedese, Carlo (ETH Zurich), Salazar, Mauro (Eindhoven University of Technology)

Analysis of Nash and Stackelberg Equilibria of Autonomous Mobility-On-Demand Systems in Mixed Traffic

Scheduled for presentation during the Regular Session "Optimization of transportation infrastructure" (WeA2), Wednesday, June 18, 2025, 11:00−11:20, Jos

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on June 1, 2025

Abstract

We study optimal interventions and operational strategies for Ride-pooling Autonomous Mobility-on-Demand (R-AMoD) systems, whereby self-driving cars provide on-demand mobility to users that can either choose to pool together or alone, in the presence of private car users that selfishly optimize their routing choices. Specifically, we first frame the ride-pooling problem and operation of the R-AMoD system as a bi-level Stackelberg game, where the leader is the fleet operator controlling the R-AMoD system with the objective of minimizing the vehicle hours traveled by the fleet, and where the followers are private car owners that choose their routes by selfishly minimizing their own travel time. Second, we parse the problem and solve it with the BIG Hype algorithm: BIG Hype employs a combination of differentiation schemes to evaluate simultaneously the hypergradient and the lower-level equilibrium and its Jacobian to deliver a Stackelberg equilibrium with local optimality guarantees. Finally, we present a real-world case-study for Sioux Falls, USA, and compare our solution to a Nash equilibrium computed by iterating between the two problems until convergence.