When many travelers respond to the same guidance (e.g., route recommendations), the outcome is not simply everyone gets faster routes. Their responses interact through congestion in mobility systems. This project develops system-level models and algorithms for information design: how agencies or platforms should structure guidance so that the whole network performs better, not just individual trips.

What you might work on.

You will build equilibrium models that represent different levels of responsiveness to guidance. You will then study how changing the information policy (e.g., what is communicated, how precise it is, and how it is delivered) affects congestion outcomes, reliability, and distributional impacts across travelers. The project gradually moves toward designing information policies using optimization methods and testing them on real-size networks.

Why it matters.

As platforms become central in shaping mobility, we need scientific tools to prevent guidance from causing harmful feedback loops, instability, or inequities—and to design guidance that is robust and beneficial.

Who we are looking for.

Students who like systems thinking, networks, game theory, and optimization. A good fit if you enjoy turning a complex socio-technical problem into a clear model, and then building algorithms and numerical experiments to answer it.