Researchers Predict Morning Traffic By Plugging Into Household Energy Use

To predict when morning traffic is likely to grind to a halt, it may be more effective to examine how electricity is used in the middle of the night instead of travel-time data. By analyzing household electricity use in Austin, Texas, researchers at Carnegie Mellon University were able to predict when morning traffic would jam some segments of Austin's highways. Predicting when traffic congestion will start and how long it will last is difficult because of day-to-day variations. Analyzing real-time travel data doesn't provide enough information for prediction purposes because drivers' departure times and traveling behaviors vary, creating ever-changing demands on highway systems. So, to better understand traffic flow, researchers explored the interrelationships between urban systems, a key concept in smart city research, by examining how Austin's transportation system intertwines with its electricity system. In this study, Sean Qian, an assistant professor of civil and environmental engineering and Ph.D. student Pinchao Zhang created a model that mined electricity-use and then employed artificial intelligence to predict traffic flow.