Multilevel Spatial–Temporal Joint Large Language Model for Traffic Prediction in Symbiotic IoT

The Symbiotic Internet of Things (IoT) represents a collaborative framework wherein edge devices and AI models coordinate resource utilization and interact via 6G connectivity to optimize operational performance and system efficiency. Given the interdependent nature of this symbiotic relationship—where the performance and efficiency of each participant are significantly enhanced by the others—accurate traffic prediction becomes crucial. Despite the ability of existing deep learning models to model spatial–temporal dependencies, they face challenges in feature engineering and natural language feature fusion, particularly in few-shot learning scenarios. The recent advancement of pretrained large language models (LLMs) has demonstrated superior performance in time series analysis through their language comprehension and generalization capabilities. This work presents an innovative multilevel spatial–temporal joint LLM (MSTJLLM) designed for traffic forecasting. The model incorporates multilevel embeddings of information flow, integrating historical features, dynamic spatial–temporal features, and prompt text features to capture complex dependencies. This approach aids LLMs in better understanding information flow through prompt text. Fine-tuning strategies enable the LLM to maintain language comprehension while enhancing spatial–temporal prediction accuracy. Tests on three real-world information flowsets demonstrate MSTJLLM’s robust prediction capability in both data-rich and data-scarce scenarios.