Feasibility of Mobile Point-to-Point Speed Enforcement: Real-World Evidence from Urban and Rural Queensland

Speeding is one of the most common traffic violations in the world, and it is a leading cause of fatal crashes. For decades, road safety experts have relied on a simple idea to stop it: if drivers believe they will be caught, they will slow down. This concept, known as deterrence, works best when the threat of being caught feels real and constant. Traditional speed cameras are fixed in place, often on tall poles or bridges. Drivers quickly learn where these cameras are, either by seeing them or by using navigation apps that broadcast their locations. As a result, many drivers engage in a pattern of "kangaroo driving," where they brake hard just before a known camera, pass it safely, and then speed up again immediately after. This behavior defeats the purpose of the camera, as it only slows traffic for a few hundred meters rather than encouraging safe driving across the entire journey.

To solve this, engineers developed point-to-point speed enforcement. Instead of measuring how fast a car is going at a single instant, this system calculates the average speed of a vehicle over a long stretch of road. If a driver enters a zone at one camera and exits at another, the system knows exactly how long the trip took. If the time taken is too short, the driver was speeding on average, regardless of how fast they were going at any specific moment. While this method has proven effective when installed on permanent infrastructure, those installations are expensive and usually limited to major highways. This leaves a gap in safety for regional and remote roads, where fatal crash rates are often much higher, but where building fixed camera towers is not practical. The question researchers faced was whether this powerful "average speed" technology could be made mobile, using cameras mounted on ordinary parked cars, to cover these dangerous, hard-to-reach areas.

A team of researchers from the University of the Sunshine Coast in Australia set out to test if this mobile approach actually works in the real world. They did not try to measure whether the cameras stopped people from speeding; instead, they wanted to know if the system could successfully track vehicles and calculate their speeds accurately across different environments. Over a six-month period, they deployed three research vehicles equipped with automatic number plate recognition cameras to twenty-four different sites across Queensland. These sites ranged from busy major cities to remote highways, covering a vast array of road conditions. The cameras were set up in pairs or groups, sometimes just a few hundred meters apart and other times more than one hundred kilometers apart. As thousands of vehicles passed these cameras, the system recorded their license plates and the exact time they passed. The researchers then used a computer to match the plates, linking the same car from the first camera to the second to calculate how long the journey took.

The results showed that the mobile system was fully capable of operating in diverse conditions. Across all the sites, the cameras detected nearly 283,000 vehicles. Of these, the system successfully matched about 33 percent, meaning it could track the same car from one camera to the next and calculate its average speed. This success rate varied significantly depending on the location. In remote areas, where roads are long and straight with few places to turn off, the system matched nearly half of the vehicles. In regional towns, the rate was lower, and in major cities, it was the lowest. The researchers discovered that the distance between the cameras was not the main reason for these differences. Instead, the key factor was how many exits a driver had to leave the monitored zone. In cities, a driver could easily turn off the main road onto a side street, exit the zone, and avoid being tracked by the second camera. In remote areas, there were very few places to turn, so once a car entered the zone, it had to stay on the road until it reached the next camera. This finding is crucial because it means that for mobile enforcement to work, planners should choose road segments where traffic is forced to stay on the path, rather than worrying about how long the distance is between the cameras.

The study also compared the mobile average speed data with traditional "spot" speed measurements taken by a laser device at the same locations. The difference in what the two methods saw was striking. The laser, which measures speed at a single moment, found that about 22 percent of the vehicles were speeding. In contrast, the mobile point-to-point system, which measures the average speed over the whole journey, found that only about 10 percent of the vehicles were speeding. This gap reveals that the two technologies are measuring different behaviors. A driver might speed up quickly to pass a laser gun but then slow down for the rest of the trip, or they might drive slowly through a city but speed up on a long, empty stretch of highway. The laser catches the momentary burst of speed, while the mobile system catches the sustained behavior of the entire trip. This confirms that average speed enforcement is not just a different way of measuring the same thing; it is a fundamentally different tool that targets a different kind of driving habit.

The researchers also tested the system at night and found that the cameras worked just as well in the dark as they did in the daylight. They even experimented with different camera layouts, such as placing cameras on three different roads entering a town. In one instance, this layout failed because the town had so many hidden exits that most cars disappeared before reaching the next camera. In another instance, where the roads converged tightly, the system worked well. These tests highlighted that the geometry of the road network matters more than the technology itself. If the road network allows a driver to easily slip out of the monitored area, the system cannot track them. However, if the road forces the driver to stay on course, the mobile system can track them effectively, even over distances as long as 112 kilometers.

Ultimately, this research proves that mobile point-to-point speed enforcement is a viable and practical method for road safety. It can be set up quickly in places where building permanent towers is too expensive or difficult, extending the reach of safety measures to the remote roads where crashes are most deadly. The study clarifies that the success of such a system depends less on how far apart the cameras are and more on choosing road segments where drivers cannot easily escape the zone. By capturing the true average speed of a journey rather than just a fleeting moment, this technology offers a way to encourage drivers to maintain safe speeds for the entire trip, rather than just slowing down when they see a camera. While the study did not measure whether this new method reduced the number of accidents, it established that the method works as a measurement tool, paving the way for future tests to see if it can truly change driver behavior and save lives.