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

ArXi:2507.19737v2 Announce Type: replace-cross The vulnerability of cities has increased with urbanization and climate change, making it 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