By extrapolating the times people are at a standstill at traffic lights, we spend around two weeks (!) of our lives waiting for the red lights to change. But that could soon be a thing of the past. AI in traffic lights is intended to help to optimise traffic light switching systems and traffic flows.
Who hasn’t experienced this? You drive home at night through streets empty of people and cars – and then come to a halt in front of a red light at an equally empty traffic light intersection. It feels like an eternity at the very least but in fact it is the normal traffic light switching time. Who hasn't wondered whether there's a better way of handling this?
At the moment many traffic lights still run a classic programme, the standard traffic light switching time. Some traffic lights are equipped with an induction loop (or a camera). Such traffic lights are then only integrated into the traffic light circuit when required. But this programming is light years away from an intelligent switching system.
Although traffic lights can already be reprogrammed using control systems, artificial intelligence (AI) can use logical calculations to see into the future and make car journeys more relaxed in time to come. And: it can reduce waiting times!
The key to optimising traffic flow lies in analysing and adjusting control of traffic lights. The way it works is easily explained:
AI in traffic lights uses sensors, cameras or other detection devices to gather information about the current traffic situation. This data may include the number and type of vehicles, the speed at which they are driving, their direction of travel and the traffic density. The traffic data that has been collected in this way is then sent to a central processing unit where AI algorithms analyse the said data so as to identify traffic patterns, but also traffic trends. This data is then used as a basis with which to adjust the various traffic light phases. Such adjustments may include lengthening or shortening the green phases for certain directions of travel or they could make the traffic light phases more coordinated.
The goal is obvious: the flow of traffic is to be optimised – and in the future this can also be calculated in real time. This means that nobody will be waiting at a red light when there is no traffic on the other roads in the same scenario.
AI in traffic lights can also communicate with vehicles and other road users (e.g. via the so-called Car-2-X communication) in order to exchange information about the current traffic situation. This data can be used to further optimise traffic flow and to provide vehicles with information about upcoming traffic light phases or congestion. This will also bring advantages for route planning in the future.
Car-2-X communication, also called Vehicle-to-Everything (V2X) communication, is a wireless communication system that networks vehicles, traffic infrastructure and other road users. Car-2-X communication can also be an important component of AI in traffic lights if the lights are included in the networking system. Then, for example, in compatible vehicles, the length of the red phase can be shown. There were test trials in this direction in Berlin a good many years ago.
By using artificial intelligence in traffic lights and networking with the appropriate vehicles, traffic jams can be avoided and traffic flow improved. The intelligent control of traffic light phases helps to reduce unnecessary waiting times at intersections. This allows vehicles to navigate their way through traffic more quickly and efficiently, which in turn saves time and fuel and, thus, not only protects the environment but also one’s wallet – a classic win-win situation.
One of the biggest benefits of AI in traffic lights is the reduction of environmental impact. The optimised traffic flow and shorter waiting times at traffic lights reduce fuel consumption. This leads to lower CO2 emissions and consequently contributes to climate protection.
AI in traffic lights is a promising approach to improving the traffic situation in cities and also to reducing environmental pollution. In combination with other technologies such as autonomous driving and smart traffic guidance systems, the intelligent traffic infrastructure of the future can make a decisive contribution to mobility transition.