High-Risk Traffic Crash Pattern Recognition and Identification Using Econometric Models and Machine Learning Models
Runan Yang, Ph.D.
Friday, March 4, 2022
12:00 PM – 1:00 PM EST
Roadway crashes have become a major cause of human deaths and injuries and caused much economic damage. According to World Health Organization (WHO), about 1.35 million people died in traffic crashes, and over 50 million people were injured from traffic crashes in the year 2016. To prevent injuries and reduce the economic loss due to traffic crashes, scholars and researchers have proposed innovative approaches to mitigate traffic crashes. This presentation covers three topics in transportation safety: effects of street lightings on nighttime crash risks, recursive bivariate probit analysis of fatalities and improper actions in motorcycle-vehicle crashes on horizontal curves and combining structured data and satellite images for crash hot-spot identification using Florida data.
Runan Yang, Ph.D., is a graduate research assistant at CUTR. For the last four years, she has worked on multiple transportation research projects such as Street Lighting Measurements on Selected Corridors in FDOT District 7, Application
of Dynamic Crash Prediction Methodologies to FDOT Safety and Transportation System Management and Operational (TSM&O) Programs, and others. Her research interests include transportation safety, motorcycle safety, crash modeling, machine learning, transportation data visualization, and analysis. She is finishing her doctorate degree in civil engineering at the University of South Florida.
