Hierarchical Strategic Decision-Making in Layered Mobility Systems
This work addresses policy design for urban mobility systems, offering a model-free approach to steer competition toward cooperative outcomes, though it is incremental as it combines existing hierarchical game theory with feedback optimization.
The paper tackled the challenge of controlling complex mobility systems with multiple stakeholders by modeling urban mobility as a tri-level Stackelberg game and using a feedback optimization approach. The method achieved substantially better municipal objectives than Bayesian optimization and Genetic algorithms on a real multimodal network in Zurich, increasing multimodal usage and improving operator objectives.
Mobility systems are complex socio-technical environments influenced by multiple stakeholders with hierarchically interdependent decisions, rendering effective control and policy design inherently challenging. We bridge hierarchical game-theoretic modeling with online feedback optimization by casting urban mobility as a tri-level Stackelberg game (travelers, operators, municipality) closed in a feedback loop. The municipality iteratively updates taxes, subsidies, and operational constraints using a projected two-point (gradient-free) scheme, while lower levels respond through equilibrium computations (Frank-Wolfe for traveler equilibrium; operator best responses). This model-free pipeline enforces constraints, accommodates heterogeneous users and modes, and scales to higher-dimensional policy vectors without differentiating through equilibrium maps. On a real multimodal network for Zurich, Switzerland, our method attains substantially better municipal objectives than Bayesian optimization and Genetic algorithms, and identifies integration incentives that increase multimodal usage while improving both operator objectives. The results show that feedback-based regulation can steer competition toward cooperative outcomes and deliver tangible welfare gains in complex, data-rich mobility ecosystems.