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New Tools for Managing Traffic on Interstate Highways

Posted: Jan 16, 2024

Thanks to University of Utah research, traffic cameras coupled with the power of artificial intelligence tools like deep learning and computer vision are giving traffic managers more reliable tools for managing traffic flow on interstate highways. The researchers used surveillance camera videos from four locations along Interstate Highway 15 in Utah to develop and evaluate a framework for using object detection and tracking algorithms to monitor traffic on highway on-ramps. The framework uses the videos as input to the framework and determines the highway on-ramp queueing parameters such as queue length and queuing time, which is important information for optimizing signal timing. Additionally, this study provides a detailed implementation plan for the use of computer vision, identifies the optimum locations for camera installations, and defines hardware requirements. Replacing conventional in-road sensors with existing traffic cameras will minimize installation and maintenance costs and limit traffic disruptions.

Nikola Markovic, Ph.D.
University of Utah

Evaluating Different Methods for Estimating Queue Length on Access Ramps

NDSU Dept 2880P.O. Box 6050Fargo, ND 58108-6050