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Researchers (from left) Ratna Babu Chinnam, Mark Nejad, and Lena Mashayekhy.

Researchers at the University of Delaware have developed a way to reduce the amount of data associated with intelligent transportation systems.

Credit: UDaily (DE)

University of Delaware (UD) researchers have developed a method to reduce the amount of data associated with intelligent transportation systems (ITS), which often struggle to provide accurate, real-time information to users.

The new approach, called hierarchical time-dependent goal directed (HTGD), involves identifying similar "communities" in the traffic data and then finding the shortest route at the highest level, which reduces the search space by eliminating entire communities that would not be traversed by the optimal path.

"We believe that the significant reduction in memory requirements of HTGD compared with those of other current methods makes it suitable to be incorporated into vehicle routing-navigation systems," says UD professor Lena Mashayekhy.

She says experimental evaluations of the HTGD method on Detroit, New York, and San Francisco road networks have demonstrated the computational efficiency and accuracy of the system.

"It will be especially valuable for determining which routes are available--and which are not--in routing emergency vehicles and organizing natural disaster evacuations," Mashayekhy says.

From UDaily (DE)
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