Event-CausNet: Unlocking Causal Knowledge from Text with Large Language Models for Reliable Spatio-Temporal Forecasting

TL;DR

Event-CausNet integrates causal knowledge extracted from large language models into spatio-temporal forecasting models, significantly improving prediction accuracy during non-recurring traffic events by combining causal reasoning with neural networks.

Contribution

The paper introduces Event-CausNet, a novel framework that leverages large language models to incorporate causal knowledge into GNN-LSTM networks for more reliable traffic forecasting during disruptions.

Findings

Reduces MAE prediction error by up to 35.87%.

Outperforms state-of-the-art baselines in real-world datasets.

Provides interpretable causal reasoning for traffic prediction.

Abstract

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.