I recently presented our collaborative research, “Discovering Traffic Breakdown Causes Using Explainable AI,” at the International Symposium on Transportation Data & Modelling (ISTDM) in Montreal, Canada. This work was led by Zhaobin Mo, Ph.D., during his internship at Argonne National Laboratory. The study explores the use of interpretable machine learning techniques to uncover the precursors of traffic breakdowns, with implications for smarter infrastructure and traffic management.
Presentation at the Sustainability Research and Innovation Congress (SRI) Conference