Cathy Wu

Guest Cathy Wu, Professor focusing on building machine-learning for future roadways and infrastructure, discusses the potential of using machine learning to predict ideal infrastructure, eliminate traffic congestion, and ensure smooth travel and safe roadways as transportation evolves. They explore the potential of self-driving cars to improve traffic situations, using existing cars and infrastructure for traffic control, and the future of transportation infrastructure and algorithmic solutions. Valuable advice for PhD students and new faculty members is also provided.

Cathy Wu, an MIT Assistant Professor, dives into her groundbreaking work using reinforcement learning to tackle mixed autonomy traffic challenges. She shares insights from her simulations of various traffic scenarios—like intersections and merges—revealing how autonomous vehicles can improve overall traffic efficiency. Wu emphasizes the surprising benefits even a few automated cars can have in reducing wait times and enhancing flow. Their interactions with human drivers and the implications for urban planning are also explored, sparking thought on the future of transportation.