AI-supported diversion management: Less traffic jams for heavy goods traffic!

AI-supported diversion management: Less traffic jams for heavy goods traffic!

Edgar Schneider, a graduate communications engineer with extensive experience in IT and production logistics, has developed an innovative concept to improve traffic management. Motivated by a report on the new B30 bridge near Hochdorf, he realized that traffic management and production logistics are comparable in many ways. Too many orders without appropriate dosage lead to traffic jams, a problem that also urgently needs to be solved in road traffic. Schneider criticizes the current diversion plans of the State Ministry of Transport, which do not provide for digital traffic control, and presents his ideas.

The “diversion management with AI” developed by Schneider focuses on heavy goods traffic and makes use of a “road ticket system”. Forwarding companies can use this system to report their trips on diversion routes in advance to the German Transport Center (VZD). This enables predetermined dosage and control of heavy goods traffic. Using AI technologies, the system can search for alternative alternative routes in real time and distribute traffic efficiently. This could result in significant traffic relief of up to 30 percent, with forecasts that a pilot project could start in 2028 during the B30 diversion.

Artificial intelligence and traffic management

The integration of artificial intelligence (AI) in traffic management is a central aspect of increasing efficiency in road traffic. The use of intelligent traffic flow control systems that reduce traffic delays is becoming increasingly important. Systems based on dynamic traffic light control analyze data in real time to adjust traffic light phases and optimize traffic flow. According to Techzeitgeist, such systems not only enable smoother traffic, but also help reduce CO2 emissions.

An example of the successful implementation of intelligent transport systems is Singapore, where a network of sensors and cameras is used to monitor and control traffic in real time. A system has also been developed in Los Angeles that combines data from over 4,500 traffic lights and numerous sensors to manage traffic more efficiently. However, the challenges of implementing these technologies, such as data protection and high investment costs, require political will and a willingness to invest in digital infrastructure.

Challenges and opportunities

Edgar Schneider points out that a bonus system for freight forwarders who follow the recommended routes would be crucial for the acceptance of his concept. While he assesses the lost toll revenue for the state as small, he sees the collection of sufficient data to optimize AI as a central point. It is clear that the use of AI in traffic management can not only help avoid traffic jams, but also proactively solve traffic problems.

Given the growing volume of traffic and the associated challenges, the future of urban mobility is closely linked to intelligent traffic flow control. Schneider offers interested parties the opportunity to request his concept by email. The success of such systems ultimately depends on the commitment of the responsible authorities and the political will.

Schneider and the numerous international examples show that the use of AI in transport is more than just a theoretical discussion; it is a pragmatic approach that could form the basis for future transport solutions. Information from [Schwäbische] and [Techzeitgeist] supports this view and shows that digital technologies can be used to sustainably relieve traffic and avoid traffic jams.