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Melbourne taps AI to ease traffic congestion

University of Melbourne has teamed up with public and private sector organisations to build an AI application that can predict traffic congestion up to three hours ahead

Australian’s University of Melbourne has teamed up with a slew of public and private sector organisations to create an artificial intelligence (AI) application that can predict traffic congestion up to three hours ahead, as well as optimise traffic and improve road safety.

The AI application, to be hosted on Amazon Web Services (AWS), can also optimise traffic signals for on-road vehicles, freight and public transport such as buses and trams.

Majid Sarvi, a transport engineering expert and director at the university’s Australian Integrated Multimodal EcoSystem, an initiative to test integrated transport technology on the streets of Melbourne, said the AI application observes the nature of traffic and figures out complex traffic patterns across the network through machine learning.

“If we can upscale the application to provide more accurate prediction with machine learning and real-time data, it will soon be possible to substantially reduce delays in hotspots across Melbourne and many locations across the globe,” he added.

PeakHour Urban Technologies, a Melbourne-based AI specialist with a focus on transportation, developed the application’s AI core engine which uses AWS to power its predictive capabilities. The company is also using AWS to ingest, store and process large amounts of traffic data.

“Pioneering AI in forecasting real-time traffic lies at the heart of this effort,” said Omid Ejtemai, founding CEO of PeakHour Urban Technologies.

“We are using a multi-disciplinary approach, combining deep knowledge of mobility with vast amounts of real-time data analytics to predict and optimise traffic in large cities.”

The Victorian department of transport provided traffic data and insight to support the creation of the application.

Victorian minister for transport Ben Carroll said managing a complex transport network presents many real-time challenges.

“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,” Carroll said.

Besides Melbourne, other cities in Asia-Pacific have also started to harness AI to improve local transportation systems.

In Malaysian capital city Kuala Lumpur, Alibaba’s City Brain AI platform is being used to analyse massive amounts of real-time data generated by 382 camera feeds and 281 traffic light junctions.

By linking up with urban management systems – including emergency dispatch, ambulance call, traffic command and traffic light control – the platform was expected to optimise Kuala Lumpur’s traffic flow and traffic signals, as well as identify the quickest routes that emergency vehicles can take to avoid gridlock traffic.

City Brain made its debut in Alibaba’s home city of Hangzhou in September 2016 and has since increased average travel speeds at traffic-clogged junctions by up to 50%.

Wang Jian, chairman of Alibaba’s technology steering committee and the brains behind City Brain, said this was accomplished not by diverting traffic from one part of the city to another, but by leveraging data analytics to control traffic light timings and other factors that affect traffic.

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