Imagine this: you're rushing to catch the subway, but the platform is packed—delays have thrown everything off. Now, imagine an AI that could predict these passenger flow surges during disruptions, helping transit authorities manage the chaos. That's precisely the goal of exciting new research from Shenzhen Technology University. Researchers have developed a clever system that leverages the power of large language models (LLMs), the same tech behind tools like ChatGPT, to forecast passenger flow in real-time when subway delays strike. The challenge? Traditional methods struggle because delays are, thankfully, rare. This makes it hard for standard AI models to learn the patterns of disruption. The Shenzhen team's solution involves "prompt refinement." Essentially, they've found a way to feed LLMs the right information in the right way, allowing the AI to understand the ripple effects of a delay. This involves translating complex data about the delay—its cause, location, time, and affected lines—into language an LLM can understand. The system then combines this with historical passenger data to make remarkably accurate predictions about where passenger bottlenecks will occur. In tests using real-world data from the Shenzhen Metro, the LLM-powered system outperformed traditional forecasting models, especially during peak disruption periods. This research offers a glimpse into a future where AI helps keep our commutes smooth, even when the unexpected happens. While the system still has room for improvement (it sometimes makes unrealistic predictions, a common LLM quirk), it’s a significant step toward smarter, more resilient public transportation.