Communications of the ACM
Using AI to Predict and Ease Congestion
The application can optimize traffic signals for on-road vehicles.
Credit: Getty Images
A project led by the University of Melbourne uses artificial intelligence to predict traffic congestion up to three hours in advance, and optimizes traffic signals for on-road vehicles, freight, and public transport.
"The application observes the nature of traffic and figures out complex traffic patterns across the network through machine learning built into the technology," says Professor Majid Sarvi, director of the Australian Integrated Multimodal EcoSystem.
PeakHour Urban Technologies developed the application's AI core engine. "We are using a multidisciplinary approach, combining deep knowledge of mobility with vast amounts of real-time data analytics to predict and optimize traffic in large cities," says PeakHour CEO Omid Ejtemai.
From University of Melbourne
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