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Project Details

Title:Knowledge-Based Machine Learning for Freeway COVID-19 Traffic Impact Analysis and Traffic Incident Management
Principal Investigators:Pan Lu and Xianfeng "Terry" Yang
University:North Dakota State University and University of Utah
Grant #:69A3551747108 (FAST Act)
Project #:MPC-657
RiP #:01775904
RH Display ID:158955
Keywords:COVID-19, demand, freeways, incident management, machine learning, predictive models, traffic incidents, traffic models, traffic volume, travel patterns


The U.S. Department of Transportation needs to quick response and adapt to the coronavirus (COVID-19) to ensure continuation of critical infrastructure support and relief for the American people. The COVID-19 has placed significant impacts to the traffic across the U.S. It is clear to see that traffic pattern, traffic demands, and duration alter with COVID status. Therefore, there is a critical research needs of studying the impact of COVID on traffic patterns and analyzing the relationship among traffic demand patterns, daily confirmed cases/death, state policies, public perception, etc. An effective model, based on the principle of newly invented knowledge-based machine learning, will be developed to predict the traffic impact of traffic incidents and advance traffic incident management (TIM) considering long-term impact of COVID on traffic.

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