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 such as Google Maps and Waze—helping drivers avoid traffic congestion, road hazards and areas prone to intense morning sun glare.
From fixed sensors to moving ones
For decades, Texas highways have relied on sensors buried in pavement or mounted along roadsides to count cars and measure speeds. While useful, these sensors only provide a snapshot at fixed points, leaving large gaps between them where conditions can change. They are also expensive to install and maintain.
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New cars are changing the game. Equipped with cellular modems and onboard systems, they continuously transmit data back to manufacturers, which use the data to improve vehicle performance, catch software bugs and offer owners proactive maintenance alerts. Automakers have now begun sharing anonymized versions of that data with government transportation agencies.
"Once you start your vehicle engine, the data collection begins," said Li. "That means the data can start even from the driveway."
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For transportation agencies, that information offers a new way to monitor roads. Connected-vehicle data currently represents about 5% to 10% of all vehicles on the road, a large enough sample to draw statistically reliable conclusions about traffic patterns, dangerous stretches of highway and emerging problems in real time.
So much data, so little time
The challenge, Li explained, is not a lack of data, but having the tools to use it effectively. The volume of information streaming in from thousands of vehicles is enormous, and the agencies responsible for managing Texas highways, such as TxDOT, do not have the time or technical resources to sift through raw data files.
Commercial cloud-based solutions exist but are prohibitively expensive, even for a large state agency like TxDOT, where costs can grow exponentially as data volume increases.
Li's project addresses both problems. The research team is developing two key tools:
•A high-performance data processing engine that runs on local computing resources rather than expensive cloud systems. It processes the incoming data stream in real time and compresses it, reducing the raw data by up to 99% so agencies receive only what they need.
•An easy-to-use online dashboard that lets transportation officials explore traffic conditions simply by clicking on a map.
Despite the vast amount of information collected, Li noted that the system does not track individual drivers. By the time data reaches TxDOT, all personal information has been stripped out.
"It will not track you individually so it totally complies with related laws and regulations,," Li said. "In the meantime, the data sample size is large enough to eliminate individual randomness and give us a statistically sound picture of what is going on on the roads."
Beyond its immediate benefits to TxDOT, Li envisions the platform becoming a broader resource for transportation researchers across Texas. The goal is to build something general enough that other teams studying connected-vehicle data can use the same tools to visualize and share their findings, accelerating the pace at which new safety ideas can make it from the lab to the road.
"We are hoping to deliver a general platform that can extend beyond our own research," Li said. "As long as it is related to connected vehicles, we hope we can become a vehicle for others to facilitate research."
About The University of Texas at Arlington (UTA)
The University of Texas at Arlington is a growing public research university in the heart of Dallas-Fort Worth. With a student body of over 42,700, UTA is the second-largest institution in the University of Texas System, offering more than 180 undergraduate and graduate degree programs. Recognized as a Carnegie R-1 university, UTA stands among the nation's top 5% of institutions for research activity. UTA and its 300,000 alumni generate an annual economic impact of $28.8 billion for the state. The University has received the Innovation and Economic Prosperity designation from the Association of Public and Land Grant Universities and has earned recognition for its focus on student access and success, considered key drivers to economic growth and social progress for North Texas and beyond.
