Knowledge-Based Machine Learning for Freeway COVID-19 Traffic Impact Analysis and Traffic Incident Management

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.

• Record URL:
  https://www.mountain-plains.org/research/details.php?id=554

Language

• English

Project

• Status: Active
• Funding: $337686
• Contract Numbers:

  69A3551747108

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108
• Project Managers:

  Tolliver, Denver

• Performing Organizations:

  Upper Great Plains Transportation Institute

  North Dakota State University
  Fargo, ND  United States 

  Dept. of Civil and Environmental Engineering

  University of Utah
  Salt Lake City, UT  United States 
• Principal Investigators:

  Lu, Pan

  Yang, Xianfeng

• Start Date: 20210624
• Expected Completion Date: 20220731
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: MPC-657

Subject/Index Terms

• TRT Terms: COVID-19; Demand; Freeways; Incident management; Machine learning; Predictive models; Traffic incidents; Traffic models; Traffic volume; Travel patterns
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01775904
• Record Type: Research project
• Source Agency: Mountain-Plains Consortium
• Contract Numbers: 69A3551747108
• Files: UTC, RIP
• Created Date: Jun 30 2021 12:08PM