Stephen Smith is a research professor of robotics at Carnegie Mellon University. He worked on a team that developed an adaptive traffic signal network called Surtrac, which improves commutes during peak traffic times in Pittsburgh. AI systems collect real-time data on where cars are on the road to change the traffic light signals based on where traffic is the worst.

The city partnered with his team after a pilot program showed it improved traffic flow and reduced average travel times by 25%.

Not only does this reduce the number of headaches for commuters, but cars spend up to 40% less time idling, reducing that much more carbon emissions from the air.

“There are technologies out there that will adapt the traffic signals to the traffic, but they tend to be applied to more suburban corridors where you have a main drag and then maybe side streets,” Smith said. “The problem we’re solving is one where you have multiple competing, dominant flows that change throughout the day, so you can’t really decide in advance where your dominant flow is. The system has to recognize that in real-time.”