The U.S. Department of Energy has awarded the University of Tennessee at Chattanooga, along with the University of Pittsburgh, Georgia Institute of Technology, Oak Ridge National Laboratory and the City of Chattanooga, $1.89 million to create a new model for traffic intersections that reduces energy consumption.
UTC’s Center for Urban Informatics and Progress will leverage its existing smart corridor to accommodate the new research.
“This work will contribute to the existing body of literature and lead the way for future research,” says Mina Sartipi, CUIP director and principal investigator. “Our existing infrastructure, the MLK Smart Corridor, will be the cornerstone for this work, as it gives us a precedent for applied research.”
In the DOE proposal, the research team noted the U.S. transportation sector alone accounted for more than 69% of petroleum consumption and more than 37% of the country’s CO2 emissions.
An earlier 2012 National Traffic Signal Report Card found that inefficient traffic signals contribute to 295 million vehicle hours of traffic delay, accounting for 5 to 10% of all traffic related delays.
The project will leverage the capabilities of connected vehicles and infrastructures to optimize and manage traffic flow.
The researchers note that while adaptive traffic control systems have been in use for a half-century to improve mobility and traffic efficiency, they weren’t designed to address fuel consumption and emissions. Likewise, while automobile and vehicle standards have increased significantly, inefficient traffic systems that increase idling time and stop-and-go traffic have hampered their potential for greater improvement.
“Our vehicles and phones have combined to make driving safer, while nascent intelligent transportation systems have improved traffic congestion in some cities. The next step in their evolution is the merging of these systems through artificial intelligence,” notes Aleksandar Stevanovic, director of the Pittsburgh Intelligent Transportation Systems Lab.
“Creation of such a system, especially for dense urban corridors and sprawling exurbs, can greatly improve energy and sustainability impacts. This is critical, as our transportation portfolio will continue to have a heavy reliance on gasoline-powered vehicles for some time.”
The goal of the three-year project is to develop an ecological adaptive traffic control system that reduces fuel consumption and greenhouse gases while maintaining a highly operable and safe transportation environment.
The integration of AI will allow additional infrastructure enhancements, including emergency vehicle preemption, transit signal priority and pedestrian safety. The ultimate goal is to reduce corridor-level fuel consumption by 20%
The team consists of Sartipi, Osama Osman, Dalei Wu and Yu Liang from UTC, Michael Hunter from Georgia Institute of Technology, Stevanovic, Kevin Comstock from the city of Chattanooga, and Derek Deter and Adian Cook from ORNL.