The vulnerability of cities has increased with urbanization and climate change, making it more important to predict human mobility during extreme events (e.g., extreme weather) for downstream tasks including location-based early disaster warning and pre-allocating rescue resources, etc. However, existing human mobility prediction models are mainly designed for normal scenarios, and fail to adapt to extreme scenarios due to the shift of human mobility patterns under extreme scenarios. To address this issue, we introduce \textbf{X-MLM}, a cross-e\textbf{X}treme-event \textbf{M}obility \textbf{L}anguge \textbf{M}odel framework for extreme scenarios that can be integrated into existing deep mobility prediction methods by leveraging LLMs to model the mobility intention and transferring the common knowledge of how different extreme events affect mobility intentions between cities. This framework utilizes a RAG-Enhanced Intention Predictor to forecast the next intention, refines it with an LLM-based Intention Refiner, and then maps the intention to an exact location using an Intention-Modulated Location Predictor. Extensive experiments illustrate that X-MLM can achieve a 32.8\% improvement in terms of Acc@1 and a 35.0\% improvement in terms of the F1-score of predicting immobility compared to the baselines. The code is available at https://github.com/tsinghua-fib-lab/XMLM.

翻译：随着城市化和气候变化，城市脆弱性增加，预测极端事件期间（如极端天气）的人类移动性对于下游任务（包括基于位置的早期灾害预警和预分配救援资源等）变得更加重要。然而，现有的人类移动性预测模型主要针对正常场景设计，由于极端场景下人类移动模式的转变，无法适应极端场景。为解决这一问题，我们提出**X-MLM**，一种跨极端事件移动语言模型框架，通过利用LLM建模移动意图并在城市间迁移不同极端事件如何影响移动意图的共有知识，可集成到现有深度移动性预测方法中。该框架利用RAG增强的意图预测器预测下一意图，通过基于LLM的意图优化器进行细化，然后使用意图调制的位置预测器将意图映射到精确位置。大量实验表明，与基线相比，X-MLM在Acc@1指标上实现32.8%的提升，在预测非移动性的F1分数上实现35.0%的提升。代码已开源：https://github.com/tsinghua-fib-lab/XMLM。