Every day, people move about, commuting to work, visiting friends, attending events and going about their business. Traffic gets heavy at times and then lightens up. All around town, electric power capacity is ready and waiting at houses and businesses for whatever needs arise—even when occupants won’t be around for hours on end. What if scientists could design a better way to ration energy resources that results in less congestion, fewer power outages and lower utility bills?
Researchers at the FAMU-FSU College of Engineering and the Western Norway University of Applied Sciences are working with the City of Tallahassee to address these issues using data- driven causality analysis modeling.
“We are looking at the relationship between electricity usage and transportation,” explains Eren Ozguven, Ph.D., a professor of civil engineering at the college and lead researcher on the project. “We call this co-mobility and we are using a causality-based analysis model for electricity and traffic load forecasting. Understanding this intrinsic relationship based on data can relate to smart power and roadway usage.”
Eventually, the team hopes the assimilation of these metrics will provide municipal utilities with a new way to use information to help them better predict the fluctuating power needs of their community and recognize opportunities to improve traffic flow.
The project is part of a grant funded through the National Science Foundation and administered through the Center for Advanced Power Systems (CAPS). The $233,000 grant represents an initiative to tackle the challenges cities face as a population grows and urbanization continues.
Cities have layers of interconnected infrastructures, places, people and information. As a result, the study of mobility goes beyond just looking at transportation systems. Instead, scientists are examining infrastructure systems and information networks as well. The project, User-Centered Heterogeneous Data Fusion for Multi-Networked City Mobility (UHDNetCity) provides a unified mathematical foundation for urban mobility management.
Other than external factors such as weather and environment, human mobility is an important factor that influences the electricity consumption at different locations and times in an urban area. Human mobility represents the location of citizens at any given point of time.
“We found, for example, in the mornings when power usage goes up, traffic is low,” Reza Arghandeh, professor from Western Norway University of Applied Sciences and co-author, said.
“Then as people commute to work and traffic usage is high, power usage goes down. This relationship is a unique indicator and we are interested in what we can do with this information.”
The preliminary analysis shows potential for improvement in electricity load forecasting accuracy. Researchers hope the knowledge of the co-mobility approach will help utilities and transportation departments accurately forecast electricity consumption and traffic flow. The team hopes to help the city adopt strategies to help it become more efficient in providing power to residents, ultimately lowering costs and providing a better experience for the community.
Other authors on this study include graduate student of electrical and computer engineering and CAPS, Lalitha Madhavi Konila Sriram, industrial and manufacturing engineering student, Mostafa Gilanifar, and Yuxun Zhou from Citadel LLC.