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 the researchers created a model that mined electricity-use and then employed artificial intelligence to predict traffic flow. This groundbreaking study attempts 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. Their model categorized 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.
The study’s results 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 are not factored into the current model.
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.
News Source: https://www.cmu.edu/news/stories/archives/2018/july/home-energy-use-predicts-morning-traffic.html
Related videos:
MIT’s Deep-learning system generates Videos that predict what will happen next in a scene
Artificial intelligence predicts patient lifespans
IBM’s Machine learning model for drug discovery
Watch More Artificial Intelligence videos at our YouTube Channel Qualitypointtech
