From Gridlock to Smart Traffic: The Case for Predictive Traffic Management on ANZ roads

By Daniel Vazquez, Region Head APAC at Kapsch TrafficCom

If you spent this morning inching along Sydney’s M5 or crawling over the Auckland Harbour Bridge, you’re not alone. Urban traffic congestion across Australia and New Zealand has roared back in the past four years. According to recent data by traffic intelligence provider TomTom, Sydney’s average driver loses about 75 hours annually in traffic, with Melburnians faring even worse at 84 hours. And congestion isn’t just an Aussie problem: Aucklanders now collectively spend 29 million hours in traffic each year, averaging over 17 hours per person (person, not driver!) lost annually.

Every hour a commuter creeps along a motorway is an hour not spent working, learning, relaxing, or with family and friends. Those lost hours have real monetary value: In 2018–19, traffic congestion cost Australia over $23 billion, and forecasts warn that without change this annual cost could soar to $30–$40 billion by 2030. In New Zealand, a recent study pegged Auckland’s congestion bill at $2.6 billion per year by 2026, factoring in lost productivity, extra fuel, and wider impacts on the economy. For businesses, clogged roads mean higher operating costs and shrinking margins. Individually, the frustration and stress of daily traffic take a toll on mental health and quality of life. Societally, stop-start traffic also pumps out extra emissions, worsening urban air quality and carbon footprints.

Worse, these trends have been moving in the wrong direction. After a pandemic-related lull in 2020, traffic has rebounded with a vengeance. In a 2025 survey by the National Roads and Motorists’ Association, 81% of businesses say congestion has worsened in the previous year, and over half report their fleets spending an extra 20-30 minutes daily in traffic compared to before.

So what’s the alternative?

Part of the answer will be policy-based – for instance, the New Zealand parliament recently passed a bill that will enable use charging starting in November 2026. Under this bill, road users will be charged different rates based on time of day and location. This bill is also aimed at reducing congestion and will most likely be effective, but it is taking a long-term approach to the problem, and acceptance of such a scheme is dependent on many factors.

Another, somewhat less controversial solution is emerging worldwide: predictive, data-driven traffic management. Instead of just reacting to traffic jams after they form, what if our cities could anticipate and prevent congestion before it happens? We now have the tools – from AI algorithms to networks of sensors – to forecast traffic conditions in real time and intervene proactively.

In fact, when I speak to transport agencies, they are increasingly demanding these capabilities. The goal: spot congestion build-ups before they fully materialise and empower traffic operators to take action. That might mean automatically adjusting traffic light timings to flush heavy flows, or alerting drivers (and transit systems) to use alternate routes just in time to avert a logjam. Modern platforms can ingest a wide variety of real-time data sources – from highway cameras and loop detectors to live bus locations and crowdsourced incident reports – and combine them into a comprehensive live picture, yielding “real-time, actionable network insights” for city traffic control centers. With predictive analytics on top, the system can run countless scenarios and raise early alarms: e.g. if a concert is ending downtown and rain is starting (bad combination), or if an accident on a motorway is likely to cause spillover gridlock on arterials in 15 minutes. Crucially, the system not only warns, but also recommends responses, essentially serving as a decision-support assistant for traffic managers.

The advantages

First and foremost, such a system can give back time – arguably the most precious commodity we lose to congestion. If a city can even cut a commute by 10% through smarter routing or timing, that’s hours saved per week for millions of people. Scaling that up, the economic gains from higher productivity and lower fuel use are enormous. Real-time rerouting and signal optimisation mean less idle time and stop-start driving, which also translates to cleaner air and lower CO₂ emissions – a key win for urban sustainability. And by responding to incidents faster (or preventing secondary incidents), these systems can enhance road safety. There’s also a less tangible but important benefit: improving the commuter experience.

A predictive system that informs drivers why a delay is happening and how it’s being resolved (or even better, guides them around it preemptively) can reduce stress and build public trust that traffic agencies are actively managing the situation. In a sense, it’s about moving from manual, reactive traffic control to an era of intelligent, automated orchestration of mobility.

Not a silver bullet, but low-hanging fruit

To be clear, predictive traffic management is not a silver bullet. It works best in combination with other strategies – public transport improvements, demand-based pricing, and good urban planning that shortens trips by developing infrastructure that has a positive impact on travel times. Bu crucially, these technology-based solutions are far quicker to deploy than building new highways. Upgrading software and integrating data feeds can be done in months, whereas constructing a new tunnel might take a decade. Given how urgent the congestion problem is (and how limited urban space and budgets are), smart management is an obvious low-hanging fruit.

The ANZ of it all

The Australia/New Zealand region is well-poised to lead in this domain. We have highly data-literate transport agencies and a strong tech sector. Our cities are big and congested enough to need innovative solutions, but small enough to be agile testbeds for new ideas. It’s encouraging to see programs like New Zealand’s upcoming congestion pricing, and Australia’s various smart motorway initiatives, putting data and analytics at the center of traffic policy. The public is also increasingly aware that simply widening roads won’t fix gridlock if we don’t manage demand and optimise usage.

In the era of Google Maps and Waze, drivers are used to real-time traffic info; the next step is connecting that user-level navigation guidance with city-level control strategy. A truly smart traffic system will align what’s best for each driver with what’s best for the network as a whole. Predictive platforms, especially those that can communicate directly with connected cars and smartphone apps, are the key to that alignment. Imagine your route home dynamically adjusting to prevent you from contributing to a jam – a win-win for you and the city.