Real-time Control and Learning for Sustainable Transportation

Traffic congestion causes significant efficiency losses, wasteful energy consumption and excessive air pollution. This problem arises in many urban areas because of the continual growth in motorization and the difficulties in increasing road capacity due to space limitations and budget constraints. As a result, traffic management that aims at maximizing the efficiency and effectiveness of road networks without increasing road capacity becomes increasingly crucial.

This project focuses on mechanism design for dynamic pricing and real-time control of traffic signals based on control, communication, optimization and game theory in order to manage traffic flows in an optimal manner and minimize traffic congestion.

Tichakorn (Nok) Wongpiromsarn
Tichakorn (Nok) Wongpiromsarn
Assistant Professor

My research focuses on formal methods, motion planning, situational reasoning, hybrid systems, and distributed control systems.