## CrossTrafficLLM: Traffic Forecasting and Natural Language A new study introduces CrossTrafficLLM, a GenAI-driven framework designed to enhance Intelligent Transportation Systems (ITS). The core innovation lies in its ability to simultaneously predict future traffic conditions and generate natural language descriptions of those conditions. ## Data and Semantics Alignment The main challenge addressed by CrossTrafficLLM is aligning quantitative traffic data with the qualitative semantics of natural language. The framework uses Large Language Models (LLMs) within a unified architecture to overcome this obstacle. This approach allows the generated textual context to refine the accuracy of predictions, ensuring that reports are based on the forecasts themselves. ## Architecture and Performance Technically, the system employs a text-guided adaptive graph convolutional network to integrate high-level semantic information with the traffic network structure. Evaluations on the BJTT dataset demonstrate that CrossTrafficLLM surpasses state-of-the-art methods in both traffic forecasting performance and text generation quality. ## Implications for Intelligent Transportation Systems By unifying prediction and description generation, CrossTrafficLLM offers a more interpretable and actionable approach to generative traffic intelligence, with significant advantages for modern ITS applications.