Beyond Language Processing: LLMs Rules-Injected Instruction Tuning for Traffic Prediction

Large Language Models (LLMs) demonstrate strong capabilities in contextual integration and multi-step reasoning, which endow them with the potential to model heterogeneous traffic data. However, the knowledge acquired during LLM pre-training is primarily qualitative and broad, and does not provide the fine-grained, context-specific quantitative dependencies required for accurate traffic prediction. This mismatch between pre-training knowledge and task requirements can reduce the effectiveness of instruction tuning, the standard approach for adapting general-purpose LLMs to downstream tasks.