Spending on intelligent traffic management to grow 75 per cent by 2028

By 2028, spending on intelligent traffic management is expected to grow by 75 per cent from the $10.6 billion figure recorded in 2023. This significant growth will stem from higher levels of government funding for smart city initiatives, with transport systems taking centre stage in urban transformation projects, according to a new study by Juniper Research.

With the increase in the urban population, the number of vehicles and, consequently, the levels of congestion, intelligent traffic management is gaining more attention. When poorly managed, the circulation of cars in large cities generates major negative impacts, such as increased carbon emissions and loss of productivity.

This is why intelligent traffic management has become one of the pillars of smart city design. Using connected technologies that combine software, hardware for radio frequency identification (RFID), cameras, cloud computing for data processing and even Artificial Intelligence (AI), these management systems offer a solution for minimising congestion and improving safety on the streets for vehicles and pedestrians.

In order to meet this demand effectively, the Juniper Research survey highlights the need for cities to open up to a wide range of suppliers in order to drive innovation and interest from a variety of partners, and also to implement traffic and analytics systems at an early stage of urban planning initiatives in order to avoid costly retrofits later on. “Cities need to avoid solutions that are likely to become obsolete quickly or result in vendor dependency,” emphasises Cara Malone, author of the research.

Furthermore, in the case of cities in emerging regions with major congestion problems, the study’s recommendation is to encourage suppliers to consider creating intelligent, personalised traffic management systems. However, there are concerns surrounding the implementation of these solutions in developing regions. Vehicle driving rules and regulations in these geographical spaces can be very different, not to mention the inequality between technological infrastructures. For these reasons, suppliers must develop scalable solutions that can be customised according to local circumstances.

According to the research, intelligent traffic management can significantly reduce congestion, saving 7 billion hours worldwide by 2028, with developed regions accounting for 75 per cent of this total.

With Google and Waze’s help

A professor from the School of Civil and Environmental Engineering at UNSW, an Australian educational institution, has developed smart traffic light technology that takes into account real-time congestion information using data from navigation apps such as Google Maps and Waze.

These applications provide data that helps understand driving behaviour and congestion patterns at a considerably low cost, without relying on the installation of cameras and sensors. “The current traffic light network relies heavily on sensors to determine when and how often traffic lights should change at each junction. What’s more, it doesn’t take into account the time it takes drivers to get from junction A to junction B,” explains Professor Vinayak Dixit.

As this data is already available in navigation apps, the solution uses it to make traffic lights smarter and thus help ease congestion during peak times.

In general, the current traffic light network uses routing algorithms that programme the time intervals between one traffic light and the next. It can also use cameras to capture and analyse the size of queues, but the data is only limited to specific junctions.

The team of researchers proved that the use of crowdsourcing reduces the level of congestion. Field experiments were carried out at 30 junctions in India and Indonesia, countries known for their congested road networks. With a low-cost, open-source controller installed at the junctions, the group collected real-time information from Google data at five-minute intervals. The controller was programmed to manage bottlenecks at traffic junctions and assign longer green lights based on the data collected.

Based on this data collected in real time, the traffic lights were programmed to assign more green lights in a given area when there was greater congestion. The results showed a reduction of up to 37 per cent in delays. “What’s more, as we’re reducing congestion, we can consider an 8 per cent drop in car emissions,” adds the professor.

According to Dixit, the technology costs around a fifth to a tenth of the price of current traffic control systems and also requires less maintenance. “We don’t want to abandon traditional sensors. It’s about expanding the scope to include other data streams in the regulations governing traffic light systems,” adds the professor.