When traffic is clogged at a downtown intersection, there may be a way to reduce some of the congestion: Eliminate a few left turns. According to Vikash Gayah, associate professor of civil engineering at Penn State, well-placed left-turn restrictions in certain busy intersections could loosen many of the bottlenecks that hamper traffic efficiency. He recently created a new method that could help cities identify where to restrict these turns to improve overall traffic flow. "We have all experienced that feeling of getting stuck waiting to make a left turn," Gayah said. "And if you allow these turns to have their own green arrow, you have to stop all other vehicles, making the intersection less productive. Left turns are also where you find the most severe crashes, especially with pedestrians. Our idea is to get rid of these turns when we can to create safer and more efficient intersections."

Traffic signals serve to regulate the worst bottlenecks in highly populated areas but are not always very effective. Researchers at Penn State are hoping to use deep reinforcement learning to improve traffic signal efficiency in urban areas, thanks to a one-year, $22,443 Penn State Institute for CyberScience Seed Grant. Urban traffic congestion currently costs the U.S. economy $160 billion in lost productivity and causes 3.1 billion gallons of wasted fuel and 56 billion pounds of harmful CO2 emissions, according to the 2015 Urban Mobility Scorecard. Vikash Gayah, associate professor of civil engineering, and Zhenhui "Jessie" Li, associate professor of information sciences and technology, aim to tackle this issue by first identifying machine learning algorithms that will provide results consistent with traditional (theoretical) solutions for simple scenerios, and then building upon those algorithms by introducing complexities that cannot be readily addressed through traditional means. "Typically, we would go out and do traffic counts for an hour at certain peak times of day and that would determine signal timings for the next year, but not every day looks like that hour, and so we get inefficiency," Gayah said.