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Predicting the unpredictable: Hong Wang applies novel theory to smooth traffic flow

Hong Wang, a senior distinguished researcher at the National Transportation Research Center, uses applied mathematics and modeling to improve transportation systems.

In Hong Wang’s world, nothing is beyond control. Before joining Oak Ridge National Laboratory as a senior distinguished researcher in transportation systems, he spent more than three decades studying the control of complex industrial systems in the United Kingdom. 

Wang developed a highly regarded stochastic distribution control (SDC) theory at the University of Manchester. SDC, in simple terms, is a control design theory used to predict and manage uncertainties in complex manufacturing, energy and transportation systems. Stochastic in SDC means randomly determined or having a random pattern that can be analyzed statistically but may not be predicted precisely. SDC has applications for general modeling, data mining, filtering design and optimization for systems that are notorious for their uncertainty.

According to Wang, no system is more prone to uncertainty than transportation.

“The flow of transportation is impacted by a multitude of factors – individual reaction of human drivers, number of vehicles on the road, type of vehicle, type of road or system – so many variables and they’re all unpredictable at any point in time,” Wang explained. “The transportation system may have different impact variables, but no matter where you live, those variables all lead to uncertainty, or what I call randomness.”

Wang says that the transportation system is also by far the most complicated, in comparison to manufacturing and the electrical power grid, and it consumes a great amount of energy in the world.

“If we can manage the uncertainties associated with transportation, what I like to call ‘squeezing control,’ we not only promote a steady traffic flow pattern, but we also greatly decrease energy consumption and improve our environment,” he said.

Wang’s theory could prove invaluable to not only reducing traffic congestion but also improving the flow and communication of connected and automated vehicles.

Hong Wang is the creator of a novel stochastic control theory that can be applied to transportation systems and improve traffic flow by managing unpredictable variables.

From the Atlantic to the Pacific and beyond

The ability to apply his stochastic theory to transportation systems is what drew Wang to ORNL. A chair professor at the University of Manchester Institute of Science and Technology for more than a decade, Wang left academia in 2016 to join Pacific Northwest National Laboratory (PNNL), where he served as a laboratory fellow and chief scientist for PNNL’s Control of Complex Systems Initiative.

“Working at a national laboratory gave me the opportunity to fully apply my control theories to real world applications through research and development,” he said. “When the opportunity was presented to join ORNL and establish control systems specifically for transportation, I knew this would be the one place where my research could be thoroughly explored and developed.”

Wang was particularly interested in working at the National Transportation Research Center (NTRC), the Department of Energy’s only transportation-related user facility located at ORNL.

Since early 2019, Wang has worked within the Transportation Planning and Decision Science Group.

“My first project here is very much general in nature, in the sense that I will be developing a generic framework for transportation modeling, applying control and optimization,” he said. “I want to integrate control activities across the Energy and Transportation Science Division and ultimately, across the lab.”

Wang developed his control theory by combining applied mathematics and advanced modeling techniques.

“I’ve always been interested in mathematics and how this tool can have societal impact,” he said. “I have used process control to look at power plant systems and plant engineering and how policies and controls could be changed to improve efficiency and production.”

Wang grew up with parents and siblings who are all professors in the engineering or mathematics field, and he earned his PhD in power plant engineering. He was raised from an early age to embrace education and says he’s had a knack for finding the right opportunities at the perfect time.

“The University of Manchester, which was my work home for so many years, has produced 25 Nobel Prize winners and is one of the top schools in the world,” he said. “And now at ORNL, I am at the best place in the world for transportation research.”

Wang views the national laboratory environment as a city with a bridge that connects universities and industry.

“Leaving academia has been an adventure, but it’s all part of staying focused and moving forward and that’s what research is about,” he said. “If I look 15 years down the road, I see myself having made significant, lasting contributions to ORNL’s transportation capabilities.”

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