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

TL;DR

This paper introduces X-MLM, a novel framework that leverages large language models to improve human mobility prediction during extreme events by transferring knowledge across cities, significantly enhancing prediction accuracy.

Contribution

The paper proposes a new LLM-enhanced cross-city learning framework for extreme event scenarios, addressing the limitations of existing models in abnormal mobility patterns.

Findings

Achieves 32.8% improvement in Acc@1

Achieves 35.0% improvement in F1-score for immobility prediction

Demonstrates effective knowledge transfer across cities during extreme events

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 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.