Michael Levin
University of Minnesota, MN·Engineering
Where should a city widen a road, add a toll or build new infrastructure? Such choices shape congestion, safety and public spending for decades.
- Fields
Engineering
Mathematics & Statistics
- Topics
transportation network design
user equilibrium
bilevel optimization
- Grants
- 2 known grants, through Feb 2030
What they work on
- Develop exact solution methods for transportation network design under user equilibrium that combine high-point relaxations, value-function cuts and strong duality cuts to find provably optimal designs faster.
- Investigate convex relaxations, decomposition strategies and branching methods that strengthen convergence guarantees and scale bilevel network design to real-world transportation networks.
- Compare kinematic wave theory, the standard fluid-like traffic flow model, against public vehicle trajectory data to find where it fails to describe real, discrete vehicle traffic.
- Test how vehicle and driver heterogeneity and random travel demand and route choices affect traffic controls such as signals, ramp meters and congestion pricing, by modifying traffic flow models.
More about the research
They are hard to get right because drivers change their routes whenever the network changes. Meanwhile, the traffic signals and controls used on real roads still rely on models at least 20 years old, even though researchers have proposed more advanced ones. The lab's projects build faster planning tools that account for driver behavior, and test whether newer traffic theory actually works on public roads.
Why it matters
Transportation agencies could use faster, provably sound design tools to choose road expansions, capacity additions and tolls that make better use of public money. Knowing which traffic control theories work on real roads could help agencies adopt better signals, ramp meters and pricing, potentially reducing congestion and travel times for drivers.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Xuegang Ban
University of Washington, WA·Engineering
Adapt and scale privacy-preserving techniques so that traffic, trajectory, and naturalistic driving data can be shared securely under both centralized and distributed data-sharing models, and evaluate them on real-world transportation datasets.
Yichang Tsai
Georgia Institute of Technology, GA·Engineering
Develop a crowdsourced multi-run methodology that assesses horizontal curve safety network-wide from low-cost mobile devices, handling the variation introduced by different vehicle types, suspension properties and driver behaviors.
Zhaomiao Guo
University of Texas at Austin, TX·Engineering·Early-career
Develop network models of how decentralized transportation and power system stakeholders with incomplete information interact across space and time as electric vehicles and clean energy expand.
The school
University of Minnesota: 88 labs on PI Pages, $1.4B a year on research, 722 research doctorates a year.
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