Module 5: Machine Learning Methods for Connected and Automated Transportation

Course Overview: This module introduces the fundamentals of machine learning, focusing on its application in connected and automated transportation (CAT). Participants will explore key concepts of supervised and unsupervised learning methods, gaining insights into how these techniques are used to address real-world challenges in CAT. The module includes practical use cases in CAT that are built using the fundamental concepts covered in the module, providing a comprehensive understanding of the role machine learning plays in shaping the future of intelligent transportation systems.

Course Instructor(s): Neda Masoud, Associate Professor of Civil and Environmental Engineering — University of Michigan

Neda Masoud is an Associate Professor of Civil and Environmental Engineering at the University of Michigan. She holds a Bachelor of Science Degree in Industrial Engineering and a Master of Science degree in Physics. She received her Ph.D. in Civil and Environmental Engineering from the University of California Irvine. She is dedicated to developing operational and planning tools that streamline the evolution toward the next generation of mobility systems, which are envisioned to be connected, automated, electrified, and shared, reflecting the forefront of transportation advancements. She is a 2021 NSF CAREER Award recipient and a Cambridge Systematics New Faculty awardee by the Council of University Transportation Centers. She is an Editorial Board Editor for Transportation Research Part B, an Associate Editor for Transportation Science, and a member of the Editorial Advisory Board for Transportation Research Part C.

Winter 2026 Office Hours: Wednesday, March 4th | 1:00 – 3:00 PM ET
Link: https://umich.zoom.us/my/neda.masoud