The Dartmouth Autonomy Seminars explore how common principles of autonomy link fields such as robotics, economics, and cognition. It brings together academia and industry to discuss autonomous systems. More information can be found here: https://sites.dartmouth.edu/madcat/dartmouth-autonomy-seminar-series/ &…;

Abstract: Congestion pricing has emerged as an effective tool for mitigating traffic congestion. However, its real-world adoption has been limited by social inequity concerns, as low-income users may be priced out of certain roads. Moreover, even in settings where congestion pricing has been implemented, time-varying and congestion-dependent welfare- or revenue-optimal dynamic tolls have proved impractical. Instead, many real-world congestion pricing deployments, including New York City’s recent program, rely on significantly simpler, often static, tolls.

In this talk, I present two complementary approaches toward more practical and publicly acceptable congestion pricing design. First, I introduce a convex optimization framework, based on a novel interpolated traffic assignment problem, for designing tolls that explicitly balance efficiency and fairness in traffic routing. Second, I study the gap between simple static tolls and optimal dynamic pricing in two canonical traffic equilibrium models and show through worst-case guarantees and data-driven case studies that static tolls can retain nearly all the benefits of dynamic tolling. Together, these results highlight the practical effectiveness of simple, operationally feasible static tolls and show that well-designed congestion pricing can simultaneously advance the efficiency and equity goals of sustainable transportation.

Bio: Devansh Jalota is a Postdoctoral Research Scientist at Columbia University’s Data Science Institute (DSI) and an incoming Assistant Professor at Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering (ISyE), beginning in Fall 2026. His research develops data-driven algorithms and incentive schemes for sustainable resource allocation in sociotechnical systems, with a focus on future mobility systems and electricity markets. He received his PhD in Computational and Mathematical Engineering from Stanford University and earned a BS in Civil and Environmental Engineering and a BA in Applied Mathematics from the University of California, Berkeley.