[2507.19737] Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning

Computer Science > Machine Learning arXiv:2507.19737 (cs) [Submitted on 26 Jul 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning Authors:Yinzhou Tang, Huandong Wang, Xiaochen Fan, Yong Li View a PDF of the paper titled Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning, by Yinzhou Tang and 3 other authors View PDF HTML (experimental) Abstract: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 Ref...