UC reduces traffic with connected vehicle research

Last year the average American spent nearly 100 hours sitting idle in traffic, according to a 2018 report by INRIX. Researchers at the University of Cincinnati are making up for lost time.

UC professor of intelligent transportation engineering Heng Wei created a model and algorithm that uses data from connected vehicles to adjust the timing of traffic lights. If applied commercially, the system can reduce traffic across the country.

Wei advised his graduate student Gaurav Kashyap to transform the idea into a computer simulation. The research now forms the core of Kahyap’s master’s thesis.

“We are not just looking at one specific scenario. We are looking at the whole picture,” Kashyap said. “The uniqueness of this research is it can be implemented in any particular situation.”

Kashyap defended his master’s thesis proposal last week and will spend the next several months finding additional applications for the algorithm. The works reflects the urban impact platform of UC’s strategic direction, Next Lives Here.

Conventional traffic lights are either pre-timed or actuated. If you’ve ever been stuck at a red light late at night, the inefficiency of this system is frustratingly obvious.

These conventional detectors are faulty, said Wei. They’re exposed to the elements and can wear down over time. Rain and snow can obscure camera lenses and cracks in the ground can disrupt underground sensors. 

The roadside device, which is part of a larger field called intelligent infrastructure, is better than current detectors in several ways, Wei said.

Unlike a pre-timed device, the roadside device receives data and changes the signal based on traffic flows in real time. Also, rather than focus on a road’s single point, the device captures data from individual cars approaching and leaving an intersection at any location.

Wei and Kashyap have established a proof of concept for their algorithm. They are applying for a patent and hope to eventually publish their findings. They’re optimistic for its potential commercial uses, especially because the algorithm can be applied to any intersection.

“We wanted to be as realistic as possible,” said Kashyap. “This project is achievable. It improves the existing infrastructure and it makes the existing technology better.”  

Featured image at top: UC professor Heng Wei, left, and UC graduate student Gaurav Kashyap are exploring ways to use data to reduce traffic. Photo/Corrie Stookey/CEAS Marketing