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Artificial Intelligence Seen Helping to Reduce Pollution, Save Fuel, Ease Traffic

How Artificial Intelligence will Change the World We Know

Vehicles stopping for red lights, idling as they wait for the signal lights to change and accelerating to get back up to speed wastes fuel and adds pollutants to the air. Idling vehicles waste more than 6 billion gallons of gasoline and diesel combined every year, according to Department of Energy (DOE) estimates.  

Seeking a better way, the DOE last year awarded $1.89 million to researchers at the University of Tennessee-Chattanooga, the University of Pittsburgh, Georgia Institute of Technology, Oak Ridge National Laboratory and the City of Chattanooga to create a new model for traffic intersections that reduces energy consumption and improves the flow of traffic. 

The goal of the project is to develop an automated traffic control system that would reduce corridor-level fuel consumption by 20%, while maintaining a safe and efficient transportation environment. The researchers intend to apply AI and machine learning to support a number of smart transportation applications, including emergency vehicle preemption, transit signal priority and pedestrian safety, according to officials at Pitt quoted in an account from GCN.  

“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 AI,” stated 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,” he said, noting that transportation will rely heavily on gasoline-powered vehicles for some time.   

Oak Ridge National Lab is working on part of the problem, in a project using overhead cameras and roadway sensors to identify gas guzzling commercial trucks in traffic. AI and machine learning algorithms identify the least-efficient vehicles, then track their path and speed in order to change the traffic signals up ahead. This eliminates some degree of the inefficient starting and stopping at intersections and minimizes fuel consumption. 

The testing is being conducted on an existing smart corridor built from a 2014 partnership between the Oak Ridge National Laboratory and the Electric Power Board (EPB) of Chattanooga as part of an effort to develop new energy technologies. The corridor employs cameras, LIDAR, radar, software-defined radios, wireless communications, and sensors for air quality and audio. These collect information from their spots on poles along a 10-block section of Martin Luther King Boulevard in the city’s downtown. A 10 Gbps fiber network underlies the smart city testbed, enabling real-time data transmission.  

By AI Trends Staff