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OSU researchers create new method for monitoring campus traffic conditions
KEITH ARNOLD
Special to the Legal News
Published: August 10, 2023
Researchers at The Ohio State University say they have devised a better way to monitor traffic conditions on crowded campus-area streets with the combination of real-time data and artificial intelligence automation.
Using the cameras already installed on campus buses, the scientists demonstrated that they could automatically and accurately measure counts of vehicles on urban roadways, detect objects in the road and distinguish parked vehicles from those that are moving.
“If we collect and process more comprehensive high-resolution spatial information about what’s happening on the roads, then planners could better understand changes in demand, effectively improving efficiency in the broader transportation system,” said Keith Redmill, a research associate professor of electrical and computer engineering at Ohio State.
Redmill, lead author of a study of the subject and a member of the university’s Control and Intelligent Transportations Research Lab, said researchers previously have relied on human observers to manually identify the vehicles in videos, while sparsely placed temporary sensors provided spatial and temporal coverage.
Published in the journal Sensors, the study utilized the traffic cameras on the Campus Area Bus Service, or CABS, on the basis that the area in which the buses operate on campus resembles a small city. The data was readily available to the research team.
The system devised by Redmill and his team employs a state-of-the-art, two-dimensional deep learning model called YOLOv4 to automatically detect and track objects.
Redmill said the program also is adept at recognizing multiple objects in a single image frame.
The study suggests the system’s results bear promise for the future of intelligent traffic surveillance, researchers said.
The algorithm not only counts vehicles, a news release detailed, it is able to project real-world, bird’s-eye-view coordinates of the road network by taking advantage of streams of images, Global Navigation Satellite System measurements and regional information from two-dimensional maps.
Redmill said the system’s precision could also detect when a bus veered from its planned route and report it to a map database to log detailed information about the roadways.
With widespread deployment and integration of this proposed approach, the vast collection and complete automation of processing of the data over time would allow for more effective planning, designing and operation of roadways to mitigate heavy traffic across the country, researchers reasoned.
“Transportation planners, engineers and operators make vital decisions about the future of our roadways, so when designing transportation systems to work over the next 30 to 50 years, it’s imperative that we give them data that allows them to improve the efficiency of the system and the level of service provided to travelers,” said Rabi Mishalani, a co-author of the study and professor of civil, environmental and geodetic engineering at Ohio State.
Motorists presumably would benefit from the advancements in traffic surveillance with reduced travel times and more options to get from one place to another, the study found.
“If we can measure traffic in a way that is as good or better than what is conventionally done with fixed sensors, then we will have created something incredibly useful extremely cheaply,” Mishalani said. “Our goal is to start building a system that could do this without much manual intervention because if you want to collect this information over lots of potential vehicles and lots of time, it’s worth fully automating that process.”
The research was supported by the U.S. Department of Transportation’s Mobility21 University Transportation Center program.
Additional co-authors included Mark McCord, Ekim Yurtsever and Benjamin Coifman, all of Ohio State.
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