World-first: How AI tech will change your commute
New world-first artificial intelligence will predict the traffic on Melbourne’s roads. But how?
Melbourne City
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A world-first project launching in Melbourne will use artificial intelligence (AI) to predict traffic across the city three hours ahead of time.
University of Melbourne’s Australian Integrated Multimodal Ecosystem (AIMES) have launched a large-scale AI to avoid bumper to bumper traffic.
Transport engineering expert and AIMES Director Professor Majid Sarvi said the technology would give Melburnians who will know about congestion before it happens.
“Because you can predict with very high accuracy what is happening on the road ahead of time there’s many things you can do,” he said.
“It gives the Department of Transport a chance to be proactive.
“They can optimise signal control and lane allocation to combat that congestion even before it happens.
“It is ready today and can be used. Transport operators can use this technology to provide a better service to all Melburnians.”
Other transport apps like Google Maps and TomTom (ONE WORD) give users road predictions based on historical data.
The new AI, launching soon in Melbourne, uses real time data to predict what’s going to happen on the road hours ahead or find an alternative route.
The technology was made and produced in Melbourne and with the help of data provided by the Department of Transport a sophisticated system has been developed.
Victorian Transport Minister Ben Carroll said the new tech could lead to self driving cars on the road.
“Not only does this world first technology help Victorians navigate congestion by predicting traffic patterns hours in advance, but it paves the way to the future of connected and autonomous vehicles,” he said.
The project is a collaboration between the University of Melbourne, PeakHour Urban Technologies, the Victorian Department of Transport, and Telstra.
The technology is ready to be used and will be implemented across Melbourne with support from the Department of Transport.
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