Accurate spatiotemporal traffic forecasting is a critical prerequisite for proactive resource management in dense urban mobile networks. While Large Language Models (LLMs) have shown promise in time series analysis, they inherently struggle to model the complex spatial dependencies of grid-based traffic data. Effectively extending LLMs to this domain is challenging, as representing the vast amount of information from dense geographical grids can be inefficient and overwhelm the model's context. To address these challenges, we propose ST-Vision-LLM, a novel framework that reframes spatiotemporal forecasting as a vision-language fusion problem. Our approach leverages a Vision-LLM visual encoder to process historical global traffic matrices as image sequences, providing the model with a comprehensive global view to inform cell-level predictions. To overcome the inefficiency of LLMs in handling numerical data, we introduce an efficient encoding scheme that represents floating-point values as single tokens via a specialized vocabulary, coupled with a two-stage numerical alignment fine-tuning process. The model is first trained with Supervised Fine-Tuning (SFT) and then further optimized for predictive accuracy using Group Relative Policy Optimization (GRPO), a memory-efficient reinforcement learning method. Evaluations on real-world mobile traffic datasets demonstrate that ST-Vision-LLM outperforms existing methods by 15.6% in long-term prediction accuracy and exceeds the second-best baseline by over 30.04% in cross-domain few-shot scenarios. Our extensive experiments validate the model's strong generalization capabilities across various data-scarce environments.

翻译：精确的时空交通预测是密集城市移动网络中主动资源管理的关键前提。尽管大语言模型（LLMs）在时间序列分析中展现出潜力，但其本质上难以建模基于网格的交通数据中复杂的空间依赖性。将LLMs有效扩展至该领域具有挑战性，因为表示密集地理网格中的海量信息可能效率低下，并超出模型的上下文处理能力。为应对这些挑战，我们提出了ST-Vision-LLM，一个将时空预测重新定义为视觉-语言融合问题的新型框架。我们的方法利用视觉大语言模型的视觉编码器，将历史全局交通矩阵作为图像序列进行处理，为模型提供全面的全局视角，以支撑小区级别的预测。为克服LLMs处理数值数据的低效性，我们引入了一种高效的编码方案，通过专用词汇表将浮点数值表示为单个标记，并结合两阶段数值对齐微调过程。该模型首先通过监督微调进行训练，随后使用分组相对策略优化（GRPO）——一种内存高效的强化学习方法——进一步优化其预测准确性。在真实世界移动流量数据集上的评估表明，ST-Vision-LLM在长期预测准确性上优于现有方法15.6%，并在跨领域少样本场景中超过次优基线超过30.04%。我们的大量实验验证了该模型在各种数据稀缺环境下的强大泛化能力。