In the spring, the city of Hamm introduced its first traffic light controlled by artificial intelligence (AI) at the intersection of Heßlerstraße and Marker Allee. This system uses several cameras to detect cyclists and pedestrians early. The AI calculates when they will reach the traffic light to give them a green light preferentially. However, in recent weeks, the automated system has caused long traffic jams during rush hour, leading to complaints from drivers. The Westfälische Anzeiger and WDR reported that the AI will be temporarily paused from 3:30 PM to 5:30 PM. During this time, the traffic light will operate conventionally to allow car traffic to flow more smoothly during rush hour. In the morning, the problem is not as severe, according to a city district meeting. The traffic jams are also suspected to be related to several nearby construction sites and detour traffic. Once these issues are resolved, the AI is expected to operate again in the afternoon, but new tests will be conducted first.
Hamm now uses AI-based technology from the Munich company Yunex at two locations. The second “intelligent traffic light,” which has been managing traffic at the Ostwennemarstraße pedestrian crossing since April 12, 2024, initially showed a constant red light for cars even when no pedestrians were in sight. This problem is said to have been resolved. The exact timing of the temporary AI shutdown at Heßlerstraße is not yet known, but the city has assured that the system will be reprogrammed within the week. In Essenbach, Bavaria, a similar AI traffic light has also been causing frustration among drivers for several months.
Meanwhile, Google has been testing a new system using machine learning to optimize traffic light operations. However, the AI solution is not always more effective than traditional technology.
Overall, the introduction of AI-controlled traffic lights in Hamm has shown some benefits but also challenges. The technology aims to improve traffic flow and prioritize vulnerable road users like cyclists and pedestrians. Yet, the implementation has highlighted the complexity of integrating AI into existing infrastructure. Adjustments and tests are ongoing to find the right balance between innovation and practicality. As cities continue to experiment with AI in traffic management, the experiences in Hamm and other locations offer valuable insights into the potential and limitations of such technologies.
AI-controlled traffic systems represent a step towards smarter cities, promising efficiency and improved safety. However, the transition is not without hurdles. The need for temporary pauses and reprogramming indicates that while AI can enhance traffic management, it requires careful calibration and monitoring. The feedback from residents and the observations made during the initial phases are crucial for refining these systems.
As technology evolves, cities like Hamm are at the forefront of testing and implementing AI solutions in public infrastructure. The lessons learned from these early adopters will guide future developments and help shape the cities of tomorrow. The ongoing dialogue between technology providers, city planners, and the public will be essential in ensuring that AI serves the community effectively and equitably.