Predicting Human Mobility in Disasters via LLM-Enhanced Cross-City Learning

--The vulnerability of cities to natural disasters has increased with urbanization and climate change, making it more important to predict human mobility in the disaster scenarios 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 disaster scenarios due to the shift of human mobility patterns under disaster . T o address this issue, we introduce DisasterMobLLM, a mobility prediction framework for disaster 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 disasters 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 DisasterMobLLM can achieve a 32.8% improvement in terms of Acc@1 and a 35.0% ITH the rapid urbanization [1] and climate change [2], cities across the world are becoming increasingly vulnerable to natural disasters (e.g., heavy rains), exposing more human lives and properties at risk. To tackle these challenges, a fundamental research problem is to predict human mobility during disaster scenarios, which can support a wide spectrum of downstream emergency response tasks including location-based early disaster warning [3]-[5], pre-allocating rescue resources [6], and planning humanitarian relief [7], etc. As a classic machine learning problem, human mobility prediction has been studied for decades; however, most existing work [8], [9] has focused on normal scenarios rather than disaster scenarios. As illustrated in Figure 1(a) and (b), we employ two representative algorithms trained in the normal scenario, i.e., DeepMove [8] and Flashback [10], to predict human mobility in normal scenarios and disaster scenarios, respectively. Their performance in disaster scenarios significantly decreases compared with normal scenarios, with an average relative performance gap of 46.4% and 24.5% in terms of accuracy and mean reciprocal rank, respectively.