Smart traffic management systems apply new technologies that monitor, evaluate and manage transport networks to enhance safety and mobility, increase sustainability and city liveability, and minimise network costs.
A key application of smart traffic management in Australia is ‘smart motorways’, which use information, communication, and control systems to boost efficiency and safety.
Innovations in smart motorways encompass coordinated ramp metering, variable speed limits, lane use control, incident detection, traveller information, and CCTV surveillance.
Variable speed limits, for example, enhance road safety by reducing the speed differential between vehicles and creating more uniform traffic flows. This minimises lane changes and braking due to speed variations, while also providing drivers with more time to react to changing conditions.
Furthermore, in addition to lowering the likelihood of an impact, variable speed limits also help to reduce the severity of any collisions that do occur.
Research in the United States has demonstrated that intelligent transport systems could reduce travel times by approximately 42 per cent and prevent between 400,000 and 600,000 road accidents each year.
For context, in 2019, the US recorded approximately 12 million vehicles involved in crashes, with more than half being passenger vehicles.
A study by Juniper Research from November 2023 estimated global spend on smart traffic management would increase 75 per cent by 2028, reaching a total of US$10.6 billion.
Cara Malone, Senior Research Analyst at Juniper, explained that cities needed to avoid solutions that would likely become obsolete quickly or result in vendor lock-in.
She said: “By opening their process to a wide pool of vendors and developers, cities will encourage innovation and interest from a variety of stakeholders and partners.”
A previous Juniper report from 2021 estimated that smart traffic management systems would save cities around the world US$277 billion by 2025 through emissions and congestion reduction, an increase on the 2021 estimated savings of US$178 billion.
The research identified smart intersections – as they leverage connectivity and AI-based automation to monitor and manage
traffic flow based on real-time data – as the key technology driving these savings.
This advancement is projected to reduce the time each motorist spends in traffic by more than 33 hours annually by 2025.
Nearly all of the savings (95 per cent) can be attributed to reduced congestion, with over three-quarters of these savings expected to be realised in North America and Europe.
This is due to high vehicle usage in these regions, along with increasing investment in smart traffic management systems.
It is estimated that traffic congestion costs the Australian economy approximately $20 billion annually, a figure that is expected to more than double by 2030.
Additionally, freight costs have risen by 50 per cent over the past decade due to significant freight bottlenecks disrupting the efficient delivery of goods.
Researchers from the Indian Institute of Technology – Bombay (IIT), in collaboration with Monash University, recently developed a computationally efficient mathematical framework to evaluate decentralised traffic control policies using fewer computing resources, which they believe could accelerate the design of next-generation intelligent traffic systems.
Lead researcher Dr Namrata Gupta explained the lack of standardised benchmarks in the field was one of the main motivations behind the work.
She said: The development of traffic signal controllers is an active research area, and several new algorithms have emerged in recent years – but there are still no universal benchmarks to evaluate and compare these algorithms.
“Our vision is that multiple standardised platforms and metrics will eventually exist, allowing researchers to evaluate their algorithms rapidly before conducting richer simulations in tools like PTV VISSIM or SUMO.”
Dr Gupta added that so far, the team had tested its proposed metrics using the two-bin abstraction and validated them, providing a more realistic representation of traffic dynamics.
She said: “The two-bin model is not designed to simulate full-scale city networks – its real value lies in providing researchers with a clean mathematical lens to understand network behaviour under simplified conditions.
“For more complex, unplanned layouts, the model acts as a benchmarking tool before we move to richer, computationally heavy simulations.”