EventOD：通过大语言模型引导的语义调制实现事件感知的OD流生成
EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation
- Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）
- Zhongguancun Academy（中关村科学城研究院）
- Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院科研与技术联盟）
- Xiuzhong College, Tsinghua University（清华大学致理书院）
- Department of Automation, Central South University（中南大学自动化学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究在破坏性事件下估计OD流的问题，提出EventOD框架，利用大语言模型和轻量级适应模块，通过输入级调制实现事件感知适应，实验表明该方法能提高重建精度和分布保真度。
AI中文摘要：
在破坏性事件下估计起讫点（OD）流对灾害应对和城市韧性至关重要。现有的基于常规出行训练的深度OD模型在极端事件突然改变区域功能和人口活动时往往性能下降，而在有限的事件时间监督下为每个事件重新训练新的生成器不切实际。我们提出了EventOD，一个事件自适应的OD生成框架，它利用结构化事件语义来引导预训练的OD生成器。EventOD首先使用大语言模型从粗略的事件观测中推断区域级功能和人口控制向量。然后学习两个轻量级适应模块AlphaNet和BetaNet来校准这些语义变化的幅度，并为稀疏监督场景引入检索增强的回退路径。通过输入级调制将生成的事件条件特征注入预训练的图扩散OD模型，实现无需更新生成器参数的事件感知适应。对美国各县飓风和大流行引发的出行进行的实验表明，EventOD始终优于强大的基线，提高了重建精度和分布保真度。
英文摘要：
Estimating origin-destination (OD) flows under disruptive events is important for disaster response and urban resilience. Existing deep OD models trained on routine mobility often degrade when extreme events abruptly alter regional functions and population activities, while retraining a new generator for each event is impractical under limited event-time supervision. We propose EventOD, an event-adaptive OD generation framework that steers a pretrained OD generator using structured event semantics. EventOD first uses a large language model to infer region-level functional and demographic control vectors from coarse event observations. It then learns two lightweight adaptation modules, AlphaNet and BetaNet, to calibrate the magnitude of these semantic shifts, and further introduces a retrieval-augmented fallback pathway for scenarios with sparse supervision. The resulting event-conditioned features are injected into a pretrained graph diffusion OD model through input-level modulation, enabling event-aware adaptation without updating generator parameters. Experiments on hurricane- and pandemic-induced mobility across U.S. counties show that EventOD consistently improves both reconstruction accuracy and distributional fidelity over strong baselines. Source code is available at https://anonymous.4open.science/r/EventOD-5C11/.
