The Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Data61 has partnered with Transport for New South Wales (TfNSW) to help improve the efficiency and effectiveness of transport systems.
The Traffic Congestion Management program, which has been under way for a few years, is touted as "analysing automated end-to-end, multi-modal journey planning for operators and passengers".
Addressing the Standing Committee on Infrastructure, Transport and Cities' inquiry into automated mass transit in Australia on Friday, Dr Chen Cai, leader of the Advanced Data Analytics in Transport (ADAIT) group at Data61 and the manager of Data61's intelligent transportation system (ITS) business, said the partnership has seen the development of a prototype artificial intelligence (AI) engine for congestion management.
According to Cai, by using the tool, it is possible to model the impact of network changes or disruptions and then issue automatic journey planning information for transport operators and travellers.
"[It's about] how to bring intelligence into managing next-generation congestion, with the key idea, instead of being reactive, which is how we used to manage events in road networks, we want to be proactive," he explained.
"We want to predict what's going to happen in the future from now."
The work is centred on determining what is going to happen on the road, through using the information both organisations have accrued up until now, including real-time, historical, and passenger transport data.
"We came up with a self-learning engine, an AI engine, that can learn from what happened prior to this date," Cai said, noting that this includes insight into thousands of accidents on NSW roads.
"We have a fairly good understanding of what are the reoccurring events, so if similar happens, we know what is the best way to have -- that's where the AI engine would recommend to the operator, and once the engine detects the scenario, these are the probably outcomes and these are the most appropriate actions to handle the situation."
Information trawled through by the AI engine includes data generated by tens of thousands of detectors throughout the road network, coming from Opal, GPS devices, traffic signals, and buses. It also includes emerging sources such as mobile phones, which Cai said provides vehicle speed and congestion-related insight.
Pointing to a commercialisation opportunity, Cai said Australia has the potential to be a global leader in syncing traffic signals to in-car equipment.
"In Australia, we have very good global products -- the SCAT system that controls traffic signals in cities. Canberra is using it, and it's used in more than 200 cities in the world -- that system has been very popular," he said.
"With autonomous vehicles and so on ... a mass transport system that is going to be increasingly automated, we have the opportunity to leverage the existing products to offer the needs of infrastructure upgrades.
"In the future, when we have a lot of [autonomous] and connected vehicles, we also need an adequate and certain amount of intelligence in the control system as well to make sure we can actually maximise the benefits from it, and I think we have an opportunity, but a limited time window."
Cai also said that the University of New South Wales has been exploring trials of connected devices on heavy vehicles, passing messages between traffic signals to trucks to tell them when the signal is going to turn green, so they can make prepare for incoming traffic without stopping too early.
"In the future, we should see a larger and greater scale application and adoption of these technologies," he added.
Department of Infrastructure, Regional Development and Cities, however, believes the important factor will be how the different technologies involved in autonomous transport will play out.
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