To predict when rush hour traffic is likely to grind to a halt, a new study suggests it may be more effective to examine how people use electricity during the night, instead of travel-time data.
By analyzing household electricity use in Austin, Texas, researchers were able to predict when morning traffic would jam some segments of the city’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.
Early birds and night owls
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.
For the new study, which appears in Transportation Research Part C, researchers created a model that mined electricity-use and then used artificial intelligence to predict traffic flow in an attempt to discover spatiotemporal relations of usage patterns among transportation and energy systems.
Researchers analyzed 79 days’ worth of electricity data from 322 anonymous households in Austin, categorizing users by the time and amount of electricity they used. For example, people who presumably went to bed early were in a different category than night owls.
Using AI, the model learns critical features about user categories and how each category relates to traffic congestion, and then it makes predictions. These predictions are significantly more accurate than predictions made by only using real-time traffic data. When households switched their use patterns from day to day, that was reflected in the time congestion started.
“Our results show that morning peak congestion times are clearly related to particular types of electricity-use patterns,” says Sean Qian, an assistant professor of civil and environmental engineering at Carnegie Mellon University.
For example, one pattern consisted of households whose electricity use increased from 2 a.m. on, but then declined before 6 a.m. This could indicate that those households may have to leave for work by 6 a.m., which positively correlates to morning congestion starting earlier.
“Another feature of this study is that it requires no personally identifiable information from households,” Qian says. “All we need to know is when and how much someone uses electricity.” This demonstrates that system efficiency can be improved while keeping personal privacy protected.
The findings are compelling, but there are limitations—more data is needed. A larger sample of household data collected over a longer time period would better train the predictive capabilities of the model. Weather and incident data affect traffic, and these aren’t factored into the current model.
Further, reproducing the study in other cities may be problematic because obtaining electricity-use data from energy utility companies is difficult. In this study, Pecan Street Inc. provided the Austin electricity data through an open data sharing platform.
While the model predicts traffic congestion, it also provides proof of concept for the pairing of transportation and energy systems to predict how systems will operate. Teasing out the correlations between how people use urban systems could lead to cross-system demand prediction and management.
“We looked at energy utilization to predict the traffic. But you could also use traffic flow to predict energy utilization in advance,” says Qian, whose follow-up research explores the relationships between transportation and water/sewer systems and social media.
The National Science Foundation; Carnegie Mellon University’s Traffic21 Institute; and Mobility21, a National USDOT University Transportation Center, funded the work.
Source: Carnegie Mellon University