用大模型提取事件因果知识，提升交通预测在突发情况下的准确性。
Event-CausNet: Unlocking Causal Knowledge from Text with Large Language Models for Reliable Spatio-Temporal Forecasting
- 用大模型解析非结构化事件报告，构建因果知识库
- 在真实数据集上将预测误差降低35.87%
- 适合需要高可靠性和可解释性的交通管理系统
虽然时空图神经网络（GNN）擅长建模重复性交通模式，但在交通事故等非重复性事件中表现急剧下降。这是因为GNN本质上是相关性模型，其学习的历史模式在突发事件引入新因果因素后失效。为此，我们提出Event-CausNet框架：利用大语言模型量化非结构化事件报告，通过估计平均处理效应构建因果知识库，并通过新型因果注意力机制将该知识注入双流GNN-LSTM网络，以调整和增强预测结果。在真实世界数据集上的实验表明，Event-CausNet实现稳健性能，预测误差（MAE）最多降低35.87%，显著优于现有最先进基线。该框架弥合了相关性模型与因果推理之间的差距，提供更准确、可迁移且具可解释性的解决方案，为关键突变时期的交通管理提供更可靠的支撑。
原文摘要 · Abstract (English)
While spatio-temporal Graph Neural Networks (GNNs) excel at modeling recurring traffic patterns, their reliability plummets during non-recurring events like accidents. This failure occurs because GNNs are fundamentally correlational models, learning historical patterns that are invalidated by the new causal factors introduced during disruptions. To address this, we propose Event-CausNet, a framework that uses a Large Language Model to quantify unstructured event reports, builds a causal knowledge base by estimating average treatment effects, and injects this knowledge into a dual-stream GNN-LSTM network using a novel causal attention mechanism to adjust and enhance the forecast. Experiments on a real-world dataset demonstrate that Event-CausNet achieves robust performance, reducing prediction error (MAE) by up to 35.87%, significantly outperforming state-of-the-art baselines. Our framework bridges the gap between correlational models and causal reasoning, providing a solution that is more accurate and transferable, while also offering crucial interpretability, providing a more reliable foundation for real-world traffic management during critical disruptions.
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