Imagine a world where traffic jams are a distant memory, replaced by smoothly flowing vehicles guided by the power of artificial intelligence. This isn't science fiction; it's the potential future being unlocked by Large Language Models (LLMs) in transportation systems. While we've used statistical models and deep learning to predict traffic patterns, LLMs offer a new level of sophistication. These powerful AI models, similar to those powering ChatGPT, can analyze massive datasets of mobility information, including real-time traffic conditions, historical trends, and even textual data like social media posts about accidents or events. Researchers are exploring innovative ways to use LLMs for various forecasting tasks. Some are 'tokenizing' traffic data, converting it into a language that LLMs understand. Others are crafting clever 'prompts' to guide the models toward specific insights, like predicting congestion hotspots or optimizing traffic signal timing. LLMs can also generate 'embeddings,' rich representations of traffic data that other AI models can use for tasks like predicting human travel behavior or imputing missing data in traffic datasets. The potential applications are vast, from predicting traffic flow and human mobility patterns to forecasting demand for ride-sharing services and even filling in gaps in traffic data caused by sensor outages. However, challenges remain. Data scarcity, privacy concerns, and the need for real-time inference capabilities are hurdles that researchers are actively working to overcome. Ensuring the reliability and stability of LLM-powered transportation systems is also paramount. Despite these challenges, the future of AI-driven traffic management is bright. As LLMs continue to evolve and researchers develop more sophisticated techniques, we can expect even more accurate and insightful traffic predictions, leading to smarter transportation systems that benefit everyone.