UCF Wins National Competition to Make Driving Safer

By: Robert H. Wells on

The University of Central Florida has won a national competition to make driving safer, beating out more than 50 other teams, the U.S. Department of Transportation announced today.

“UCF’s Smart and Safe Transportation Team and I are honored to be selected by the U.S. DOT for our innovative data analytics and visualization system to help operators and decision-makers in their work to make our transportation system safer,” says Mohamed Abdel-Aty, a Pegasus Professor and chair of UCF’s Department of Civil, Environmental and Construction Engineering, who led the research team.

“The system adds many dimensions to state-of-the-art in-road safety research and practice, and changes much of our thinking from being reactive to being proactive in how we deal with traffic safety issues,” he says. “This system will be the start of more innovation and safety solutions in the near future by the SST team at UCF.”

Notice of the win of the Solving for Safety Visualization Challenge came from Elaine L. Chao, secretary of the Department of Transportation, which sponsored the competition.

“Safety is the department’s top priority and the Solving for Safety Visualization Challenge, which the University of Central Florida won impressively, is part of ongoing efforts to save lives and prevent injuries,” Chao says in a news release.

As the winner and also a semi-finalist in the competition, UCF will receive $220,000 in prize money from the department.

UCF’s entry, Real-Time Crash Visualization Tools for Traffic Safety Management, is a computer program that uses big data to predict — and hopefully prevent — traffic accidents before they happen.

Abdel-Aty’s team became a finalist in April, along with Ford Motor Co., after beating three other teams, including one from Uber, during the semifinals.

As the team advanced through the competition, it developed its entry from an idea in stage I, to a prototype for the stage II semifinals, and then created a fully working version for submission in stage III, the final part of the competition.

Using information such as real-time traffic data, weather, history of past accidents and violations, and other data, the UCF Smart and Safe Transportation team’s program predicts if the risk of an accident increases or decreases in a situation and presents the probability in an easy-to-understand visual readout alongside a map overlaid with current traffic-flow conditions.

The idea is that if the risk of an accident rises, then transportation operators could implement measures immediately to reduce the risk, such as reduced speeds, metered ramps, and messages warning drivers of perilous conditions ahead, says Abdel-Aty.

“Reducing risk can reduce the possibility or the severity of an accident,” he says.

Abdel-Aty says his team’s tool represents a proactive approach to helping vehicular travel become less dangerous by recognizing dangerous roadways, intersections, and conditions before the statistics about their hazards pile up.

“There are about 37,000 traffic fatalities per year in the United States and millions of injuries, which is really unacceptable and a huge burden on our society,” Abdel-Aty says.

He says the Smart and Safe Transportation team’s program is designed to make real-time improvements in traffic safety based on rich data and sophisticated algorithms. Seeing different trends and dynamics will allow operators to make better decisions.

Abdel-Aty received his doctorate in civil engineering from the University of California, Davis, and his master’s and bachelor’s degrees in civil engineering from Alexandria University. He joined UCF in 1995.

The research team was comprised of students and researchers from the College of Engineering and Computer Science, including students Ou Zheng, Cheng Yuan, Morgan Morris, Yaogang Gong, Jacob Lites, Jiajia Dong, Whoibin Chung, Moatz Saad, Lishengsa Yue, Jorge Ugan, Shile Zhang, Pei Li, Zubayer Islam, Md Sharikur Rahman, Md Hasibur Rahman, Ma’en Al-Omari, Ahmed Abdelrahman, Nada Mahmoud, postdoctoral associates Yina Wu and Jinghui Yuan, and professors Samiul Hasan, Jaeyong Lee and Qing Cai.

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