Student Learning Hub: Online Optimization Meets Urban Transportation

Instructor: Tao Li, New York University

Beginner level: No prior experience required. Basic optimization knowledge would be helpful but not required.

Description: Urban transportation networks are complex and dynamic, and as a result, offline planning alone may not ensure effective real-time management. Therefore, this course introduces online optimization methods with a focus on transportation applications. We’ll start with gradient descent in conventional convex optimization, move to online gradient descent, and briefly explore multi-agent online learning and associated equilibrium convergence. We’ll also discuss challenges in deploying online optimization in urban transportation.