Ever dream of a world without traffic jams? Imagine AI smoothly directing the flow of cars, trucks, and buses through busy intersections. That's the intriguing possibility explored in new research using Large Language Models (LLMs), the same technology behind ChatGPT. This study investigated whether an LLM called GPT-4o-mini could manage traffic in realistic urban simulations, including complex scenarios like heavy traffic, mixed vehicle speeds, pedestrians, and even obstacles. Researchers set up a series of virtual intersections and unleashed the LLM to analyze the traffic flow in real-time. They fed it data on each vehicle's speed, acceleration, lane position, and destination. The LLM's task was to predict potential conflicts and issue instructions to prevent collisions, like telling a car to yield or change lanes. The results? GPT-4o-mini showed remarkable promise. It successfully managed conflicts in heavy traffic and mixed-speed scenarios, navigating the virtual intersections with surprising efficiency. Even complex scenarios with multiple intersections, obstacles, and pedestrian crossings were handled effectively. While this research is still in its early stages, using simulated environments, it hints at the transformative potential of LLMs for urban traffic management. Imagine a future where AI helps prevent accidents, reduces congestion, and makes our commutes smoother. However, challenges remain, such as ensuring the LLM can react quickly enough to real-world conditions and integrating with existing traffic infrastructure. The next step is to test these AI traffic managers in real-world settings, potentially paving the way for smarter, safer, and more efficient cities.