Public and road safety heavily rely on traffic enforcement. In recent years, it has been evolving alongside tech advancements. We’ve seen the evolution from static speed cameras to modern systems based on artificial intelligence. Even though AI-powered solutions aren’t ubiquitous yet, we see they’re being tested and will be implemented all around the globe.
Many drivers are always skeptical about tech novelties. Many people are not sure if even static speed cameras are used for road safety and not just for the sake of making money. That encourages them to take advantage of anti-radar stickers which make their identifiers invisible. The question of how road users will perceive the integration of artificial intelligence remains open.
Let’s take a more thorough look at the transformation of traffic enforcement and what role artificial intelligence plays in it.
The expansion of automated enforcement
The first thing that made a major leap in automated enforcement was speed cameras. They were implemented in the late 1990s. Those are the devices with radars and cameras that capture images of fast-moving vehicles. Such technology helps to issue fines without the need for police officers to be present.
The introduction of the technology caused mingled feelings in public. On one hand, they serve as a way to ensure the safety of all road users. On the other hand, not all drivers can agree that speed cameras are 100% accurate. This encourages drivers to learn more about anti-radar stickers with Nanofilm covers that serve as speed camera protection. Moreover, drivers invest in ghost number plates as some of them believe that traffic enforcement technologies are used as money-making machines.
Impact of speed cameras
Ultimately, the prime goal of using speed cameras is to reduce fatal crashes. However, as many studies show, drivers tend to lower their speed as they approach cameras and speed up when they leave the zone. Those drivers who take advantage of legal anti-speed camera solutions don’t drop their speed even in the vicinity of cameras.
IIHS reports that cameras have reduced the likelihood of road users exceeding the speed limit by almost 60%. Moreover, it has resulted in an approximately 20% reduction in the likelihood of fatal crashes.
All studies and research show that speed cameras are effective but not 100% effective. Drivers are more concerned about not getting fined. That’s why they lower the speed when they approach cameras. As soon as there’s no sight of any camera, nothing stops them from speeding up.
Static cameras have limitations as they monitor specific locations. That’s why the usage of AI solutions would be vital for addressing dynamic traffic solutions.
Artificial intelligence in traffic enforcement
AI is known for its ability to self-learn and analyze complex data. In this regard, it can offer to process traffic patterns in the real-time mode, identify dangerous behaviors, and make sure that all road users comply with rules and regulations.
Here’s what AI-power cameras can do:
- 1. Detect automobiles when they cross intersections on the red light. Cameras can capture video evidence and it’ll be difficult to raise a dispute as there will be proof;
- 2. The technology can spot drivers who get distracted behind the wheel. It can identify road users who use their phones while driving and do not wear a seat belt. The proof can be in high-quality images;
- 3. Aggressive driving causes chaos on the roads which leads to car accidents. The systems can analyze driving behaviors when there’s sudden acceleration, hard braking, or erratic lane changes. All reckless drivers can be identified.
We’re not talking about AI being used for traffic monitoring in the future. We’re talking about technology being used already. Dubai, in this regard, acts as the most tech-advanced city. There are AI-powered radars that can capture 6 traffic violations.
According to the Gulf News, smart radars can spot the following violations:
- ❖ The usage of phones behind the wheel;
- ❖ Sudden lane deviations;
- ❖ Drivers and passengers not wearing seat belts;
- ❖ Improper lane discipline;
- ❖ Illegal tinting of windshields;
- ❖ Excessive vehicle noise.
The part of ML and Big Data
AI systems wouldn’t be so powerful without ML and Big Data as they help to improve accuracy and efficiency. Moreover, the vast amount of data these systems process helps to predict traffic jams and accidents and prevent them.
Everyone hates driving during rush hour when there are hundreds and thousands of vehicles on the roads. Lots of drivers take advantage of smart apps like Wise to reroute their trips to avoid traffic jams. The application uses crowdsourced information to warn road users about accidents on the roads and traffic slowdowns ahead.
Besides, in Singapore’s example, we can see how predictive analysis works. The city takes advantage of AI to deal with its most challenging problem. Traffic jams are widespread across large cities, especially during rush hours. One possible solution is to build more roads. However, this city has turned AI to its benefit. They leverage the technology to predict congestion and ensure the city flows smoothly. As a result, the delays have been reduced by 20% during rush hours.
