Estimation of Traffic Conflicts at Signalized Intersections Using High-Resolution Traffic Signal Data

Principal Investigator:

Gary Davis, Professor, Civil, Environmental and Geo-Engineering


  • Henry Liu, Associate Professor, Civil, Environmental and Geo-Engineering

Project Summary:

This research is exploring the possibility of using high-resolution traffic signal data, which can be directly collected from existing loop detection systems, to evaluate intersection safety. Traditional methods—either using historical crash data collected from infrequently and randomly occurring vehicle collisions, or potential traffic conflicts estimated from a microscopic traffic simulator which generally assumes "crash-free" conditions—cannot provide an accurate and timely evaluation of intersection safety. By contrast, the proposed method estimates potential traffic conflicts using high-resolution traffic signal data collected from the SMART-Signal system, which has been deployed at more than 100 intersections in the Twin Cities. Using the estimated traffic conflicts and the field-collected crash occurrence data, a crash prediction model will be built and calibrated. This work will provide a low-cost and easy-to-use toolbox for traffic engineers to evaluate traffic safety performance at signalized intersections without relying on vehicle crash event data, which usually have a long data-collection period. The potential conflicts estimated in this research include both rear-end and crossing (i.e., right-angle) conflicts. This work also has the ability to identify red-light-running events when stopbar detectors are available.


Project Details: