Artificial Intelligence in road traffic

July 7, 2020

AI Road Traffic

The transport network is clearly reaching its limits. It has been the subject of public discussion for decades. But how can traffic jams be avoided, the traffic flow be optimized and the number of traffic fatalities be reduced? It is common knowledge that changing road traffic regulations alone will not solve the problem. A comprehensive rethink is needed: more public transport, intelligent traffic networking and autonomous driving are the key words. How can artificial intelligence contribute to the optimization of road traffic?

Selective and global application

Artificial intelligence can be used both selectively and comprehensively for road traffic and especially for driving. Some of the functions in which AI is successfully used are, for example, automatic distance recognition or parking. These systems are becoming more sophisticated and precise thanks to large amounts of training data.

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Artificial intelligence has tremendous potential in road traffic. It can make driving more comfortable and safer, but also more ecological and intelligent. Advanced algorithms based on the principles of artificial intelligence, machine learning and big data take into account traffic volume and parking space situation in real time to optimize traffic flow and improve individual mobility. As a result, the user reaches his destination as quickly, safely and comfortably as possible. The general networking (“Internet of Things”) can make a significant contribution to accelerating such processes and in some cases make them possible in the first place.

AI in road traffic: acceptance is growing

How high are the acceptance rates of AI in road traffic? According to a study by the digital association Bitcom, the majority of German citizens are in favour of the use of AI to optimize traffic flow, especially in terms of increased safety. Majorities of up to 90 percent are achieved, for example, when artificial intelligence is used to

  • identify traffic jams at an early stage in order to avoid them in time,
  • provide warnings of critical situations to prevent accidents,
  • optimize traffic light signals to control the flow of traffic.

On the other hand, there is research into concepts that make the human driver partially or even completely superfluous. Respondents are rather skeptical when it comes to autonomous driving. However, there is a clear generational difference here. AI systems that are capable of taking over the steering wheel autonomously tend to have more supporters among the young than among the old.

Intelligent traffic solutions with AI

Many road traffic processes can be significantly improved. Every driver who has to wait at a traffic light for minutes on end – even though there is no apparent reason for doing so – except that the traffic light system works according to a fixed pattern that is completely independent of the current traffic situation can relate to this. The use of artificial intelligence to keep traffic moving in response to the current situation has many advantages:

  • Flowing traffic, with no traffic jams, is good for the environment. And this calls not so much for the use of hardware as for the further development of software, making it another relevant environmental aspect.
  • It enables the optimization of many business processes, such as deliveries, which is of great benefit to the economy.
  • Human error, by far the most frequent cause of accidents, can be largely eliminated by comprehensively managing the flow of traffic. Eliminating the human factor could drastically reduce the number of accidents.
  • It also presents attractive opportunities in the transport sector: the term Truck Platooning describes the concept of electronically linking several trucks driving in a convoy on the highway. Here, a human driver only sits in the leading vehicle. AI takes over the control of all the following trucks.

All these factors contribute to the optimization of the overall traffic system. Every road user benefits from this – even those who were previously only able to participate in road traffic to a limited extent without the aid of digital tools.

AI quality is decisive

The quality of the software designed for use in road traffic is influenced on the one hand by the programming of the algorithms, but also to a large extent by the quantity and quality of the training data. The more reliable and realistic training data for machine learning are, the more potential there is for safe road traffic design.

However, it is obvious that AI in road traffic has to accept many setbacks, especially in the current stage of development. Accidents caused by faulty software have made the headlines again and again. However, from a realistic point of view, these individual incidents are only partially suitable for questioning autonomous driving in principle. A final statement on the contribution of autonomous driving to road safety and the reduction of accident figures calls for a reliable comparison that puts two figures in relation to each other:

  1. How many accidents are caused by faulty programming?
  2. How many accidents occur in the same situations due to human error?

Accidents that result from software errors are closely monitored by the public. In contrast, lack of human attention as a cause of accidents rarely makes it into the headlines. However, this does not necessarily reflect the everlasting superiority of human control.


Artificial intelligence in road traffic can help identify risks in good time and make driving more comfortable and easier. AI also makes a productive contribution to the regulation of traffic flows and to the planning of entire road systems. By analyzing traffic patterns, traffic volumes and other data, the design of road networks can be adapted to the prevailing conditions – for an optimal traffic flow that pays off not only from an economic but also from an ecological point of view.


Dieser Artikel wurde am 07.July 2020 von Jan Knupper geschrieben.


Jan Knupper