After a summer that broke all sorts of dismal records in terms of cataclysmic wildfires across North America, there is now an even greater awareness of poor air quality—its myriad health impacts and the overwhelming need for sustainable solutions.
To that end, Yu Yang, an assistant professor of computer science and engineering in Lehigh University’s P.C. Rossin College of Engineering and Applied Science, is leading two research projects, with new support from the National Science Foundation, ultimately aimed at improving the air we breathe. The most recent award will fund his work using machine learning techniques to develop socially informed traffic signal control systems to reduce air pollution caused by vehicle emissions.
In dense urban areas, vehicles idling at stoplights can contribute to localized air pollution. It’s a problem for everyone—but especially for those with asthma and other health conditions that make them particularly sensitive to airborne particulate matter.
Yang and his team are developing a three-pronged method that could allow for a more consistent traffic flow with fewer and/or shorter stops to minimize polluting emissions. They’ll first develop a low-cost, mobile air-quality sensing system to identify areas of high pollution, and collect the social requirements of different areas. An area with a hospital, for example, might harbor large numbers of sensitive individuals. “We’ll use those data to then develop a spatial-temporal graph diffusion learning model to determine the traffic situation in our test-bed city of Newark, New Jersey,” says Yang. “In other words, what is both the traffic and the air pollution like at different points of time in different locations?” Finally, the researchers will use a reinforcement learning method that will incorporate traffic signals around the city, and simulate how traffic signal control helps improve air quality.
“This is the first project of its kind to incorporate a social component into a traffic control system,” says Yang. “We’re taking both a technical and a social perspective to solve a real-world problem.”
Ultimately, Yang envisions a traffic management system that would enable city transportation officials to control signals in real time, and a web-based system that would show city residents location-specific air quality levels so they can make informed decisions about what activities they do and where.
