用大模型解析事件文本，提升城市动态出行预测精度
SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction
- 通过多智能体架构自动提取文本中的时空事件信息
- 在事件附近区域预测误差降低13.92%（MAE）和11.12%（RMSE）
- 适合需要融合新闻、社交数据的城市交通研究者
人类出行预测对城市服务至关重要，但现有方法难以应对外部事件带来的突发变化。传统时空模型无法有效利用描述这些事件的文本信息。本文提出SeMob，一种基于大语言模型的语义合成框架，用于动态出行预测。SeMob采用多智能体系统，由大模型驱动的智能体从复杂在线文本中自动提取并推理时空相关事件信息。通过创新的渐进式融合架构，将细粒度上下文与时空数据结合。预训练事件先验提供丰富事件驱动洞察，使预测模型更贴合真实情况。在自建数据集上评估显示，相比传统时空模型，SeMob在平均绝对误差（MAE）上减少13.92%，均方根误差（RMSE）减少11.12%。尤其在事件发生地点及时间附近的区域，性能优势显著。
原文摘要 · Abstract (English)
Human mobility prediction is vital for urban services, but often fails to account for abrupt changes from external events. Existing spatiotemporal models struggle to leverage textual descriptions detailing these events. We propose SeMob, an LLM-powered semantic synthesis pipeline for dynamic mobility prediction. Specifically, SeMob employs a multi-agent framework where LLM-based agents automatically extract and reason about spatiotemporally related text from complex online texts. Fine-grained relevant contexts are then incorporated with spatiotemporal data through our proposed innovative progressive fusion architecture. The rich pre-trained event prior contributes enriched insights about event-driven prediction, and hence results in a more aligned forecasting model. Evaluated on a dataset constructed through our pipeline, SeMob achieves maximal reductions of 13.92% in MAE and 11.12% in RMSE compared to the spatiotemporal model. Notably, the framework exhibits pronounced superiority especially within spatiotemporal regions close to an event's location and time of occurrence.
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