UTA research turns vehicle data into traffic solutions Every time millions of Texans hit the highway, their vehicles collect what’s known as connected-vehicle data—information such as speed, hard braking and seatbelt use—and send it back to manufacturers. Now, a researcher at The University of Texas at Arlington is working to put that information to use in hopes that it leads to fewer traffic headaches. Taylor Li, an associate professor of civil engineering at UTA who specializes in transportation and intelligent traffic systems, has received a research grant from the Texas Department of Transportation (TxDOT). He is developing a faster, smarter way to process connected-vehicle data and deliver it to state agencies that manage Texas roads. The project, a collaboration with the Texas A&M Transportation Institute, aims to help transportation officials identify and address traffic issues more quickly. Why it matters for drivers For anyone who has sat in gridlock, the promise of this research is straightforward: fewer surprises on the road, faster responses when problems arise and, ultimately, safer commutes. Consider a common experience across Texas: On winter mornings, the low-rising sun can create a blinding glare at specific points along the highway, forcing drivers to brake suddenly. These hidden danger zones often go undetected by traditional traffic sensors, which only measure cars passing a fixed point. "Those locations will never be captured using traditional data," Li said. "We can find such things, help solve safety issues and make sure vehicles are moving along." The new system can also detect when vehicle speeds vary sharply along a stretch of road—a warning sign that something may be wrong. Identifying and addressing those problem areas quickly, Li said, is a “life-saving matter." Beyond helping transportation agencies spot trouble areas, connected-vehicle data can also be used to provide real-time navigation updates through apps