Smart traffic sensors that reduce gridlock – and unlock the economy


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Traffic congestion is estimated to cost the economy $20 billion a year. To help solve the problem, the University of Melbourne and Telstra have partnered on research to model the impact of smart traffic lights. This innovative technology prioritises freight vehicles at intersections, while minimising the impact on other road users.

Traffic congestion is not just an inconvenience for time-poor travellers, but it has severe ramifications for the Australian economy. In fact, it’s estimated that congestion costs the economy $20 billion annually. That number is expected to exceed $40 billion by 2030.

Meanwhile, freight costs have grown by 50 per cent in the past decade. This is largely caused by major freight bottlenecks disrupting the efficient delivery of goods throughout the country. The slower that vehicles move from A to B, the higher the costs for operators, businesses and, ultimately, consumers. The bottlenecks also have flow-on effects for all other road-users.

With Australia preparing for self-driving vehicles on our roads, can smart technology play a role in keeping traffic flowing through our complex road networks?

A team of University of Melbourne researchers has partnered with Telstra to answer this question, by modelling the impact of intelligent traffic lights that communicate with connected trucks.

While fully autonomous vehicles and connected traffic systems might seem like a distant dream, a hybrid network of smart and conventional vehicles is already the reality. Dr Renata Borovica-Gajic, a Senior Lecturer in Data Analytics in the school of Computing and Information Systems, notes that even small improvements can mitigate the environmental and economic impact of heavy traffic.

“If even just 10 per cent of vehicles on the road could be connected and send their GPS coordinates, that alone can have a big impact.

“We have seen huge benefits [to traffic flow] when 10 to 20 per cent of vehicles are being connected for solutions such as dynamic traffic signal control or lane reconfiguration. We are not talking about all or nothing.”

Getting smart about traffic

Dr Borovica-Gajic is one of the researchers behind the simulation study, which analysed how connected freight vehicles could be prioritised at intersections to optimise traffic flow.

Autonomous Intersection Management (AIM) strategies use information that is communicated from vehicles and received by sensors on traffic lights. The information adjusts what traffic is let through intersections and when.

For this research project, the AIM strategy prioritised freight trucks ahead of other road users. The researchers wanted to ensure that doing so would not then create flow-on effects that would delay traffic further along the network. In fact, by prioritising freight trucks, the aim was to ultimately improve traffic flow for all road users.

Telstra had developed the enabling technology, and initially ran a pilot project involving five traffic lights in the coastal NSW city of Wollongong. While it showed promising real-world results, Telstra needed to understand the impact that prioritising freight traffic would have on the rest of the road network.

“Telstra has really great technology in this space,” says Dr Borovica-Gajic. “They have a capability to connect freight vehicles and send their positions to an intelligent traffic management system.

“But where they needed our expertise, prior to real-life deployment, was in understanding the broader impact on city-level traffic.”

That’s where the University of Melbourne’s traffic simulator, the Scalable Microscopic Adaptive Road Traffic Simulator, otherwise known as SMARTS, came in. After the Australian Integrated Multimodal EcoSystem (AIMES) platform captures data directly from Melbourne's streets via a network of sensors, SMARTS integrates with the AIMES platform to obtain this data. SMARTS then runs a live simulation of the city’s traffic networks, creating an invaluable source of information for researchers.

“We have connected devices and smart cameras throughout the city, collecting data about traffic density,” says Dr Borovica-Gajic. “We can then leverage this data to dynamically optimise the traffic flow.”

The researchers took Telstra’s real-world data from the pilot project in Wollongong and fed it into the SMARTS traffic simulator to determine what would happen if the same strategy was deployed in Melbourne.

The results demonstrated that AIM strategies can help improve freight vehicle performance with minimal impact on other vehicles in realistic traffic environments. The aim, ultimately, is for the research to help traffic engineers develop freight vehicle prioritisation solutions. And as more real-world AIM data becomes available in the future, the researchers plan to calibrate their simulations further.

An ongoing partnership

Partnering with the University’s interdisciplinary transport research group gave Telstra access to a long-term perspective and the capacity to simulate the impact of their technology on a broad scale, says Gilbert Oppy, a Senior Technology Specialist at Telstra.

“I could bring a practical lens to the project, and act as a kind of bridge between what’s happening in industry and the research that the University of Melbourne is doing.”

With autonomous vehicles set to become more common on our roads, Oppy says it’s the kind of research that will grow in importance.

“[Autonomous vehicles are] going to get pretty big in Australia in the coming decades, and a key part of that is having vehicles that can talk to one another, and the infrastructure to make that a reality.”

Telstra and the University of Melbourne have a joint research fund, and Oppy foresees more ground-breaking research down the track.

“The researchers have been very open to industry input. They wanted to know what could be done, and what would be useful for us. Based on the success of this project, it’s hoped that we can continue in this collaborative vein.”

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First published on 18 May 2023.

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