用强化学习动态融合多图关系，提升飓风疏散交通预测精度与可解释性。
Reinforcement Learning-Guided Dynamic Multi-Graph Fusion for Evacuation Traffic Prediction
- 构建多维动态图捕捉探测器间复杂时空关系
- 1小时预测准确率95%（RMSE=293.9），6小时达90%（RMSE=426.4）
- 通过强化学习实现特征重要性排序，模型更透明易懂
实时交通预测在飓风疏散中至关重要。尽管数据驱动的图学习模型在捕捉网络级疏散交通复杂时空动态方面表现优异，但多数仅基于单一维度（如通行时间或距离）构建图结构，且缺乏可解释性。为此，本文提出一种基于强化学习的动态多图融合（RL-DMF）框架。在每个时间步构建多个动态图以表征交通探测器间的异构时空关系，采用动态多图融合模块自适应学习并整合信息。为增强可解释性，引入基于强化学习的智能特征选择与排序（RL-IFSR）方法，在训练中学习屏蔽无关特征。基于2016至2024年佛罗里达州12场飓风的真实数据集进行评估。对于未见过的飓风Milton（2024），模型在预测未来1小时交通流时达到95%准确率（RMSE=293.9），未来6小时预测准确率达90%（RMSE=426.4）。RL-DMF显著优于多个前沿交通预测模型。消融实验验证了动态多图融合与RL-IFSR的有效性。该研究为实时疏散交通预测提供了一个通用且可解释的模型，对应急交通管理具有重要意义。
原文摘要 · Abstract (English)
Real-time traffic prediction is critical for managing transportation systems during hurricane evacuations. Although data-driven graph-learning models have demonstrated strong capabilities in capturing the complex spatiotemporal dynamics of evacuation traffic at a network level, they mostly consider a single dimension (e.g., travel-time or distance) to construct the underlying graph. Furthermore, these models often lack interpretability, offering little insight into which input variables contribute most to their predictive performance. To overcome these limitations, we develop a novel Reinforcement Learning-guided Dynamic Multi-Graph Fusion (RL-DMF) framework for evacuation traffic prediction. We construct multiple dynamic graphs at each time step to represent heterogeneous spatiotemporal relationships between traffic detectors. A dynamic multi-graph fusion (DMF) module is employed to adaptively learn and combine information from these graphs. To enhance model interpretability, we introduce RL-based intelligent feature selection and ranking (RL-IFSR) method that learns to mask irrelevant features during model training. The model is evaluated using a real-world dataset of 12 hurricanes affecting Florida from 2016 to 2024. For an unseen hurricane (Milton, 2024), the model achieves a 95% accuracy (RMSE = 293.9) for predicting the next 1-hour traffic flow. Moreover, the model can forecast traffic flow for up to next 6 hours with 90% accuracy (RMSE = 426.4). The RL-DMF framework outperforms several state-of-the-art traffic prediction models. Furthermore, ablation experiments confirm the effectiveness of dynamic multi-graph fusion and RL-IFSR approaches for improving model performance. This research provides a generalized and interpretable model for real-time evacuation traffic forecasting, with significant implications for evacuation traffic management.
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