Zero-Shot Human Mobility Forecasting via Large Language Model with Hierarchical Reasoning
This work addresses the challenge of generalizing mobility predictions to new scenarios for applications like transportation planning, offering an incremental improvement through a novel method for a known bottleneck.
The paper tackles the problem of human mobility forecasting for unseen users or locations by proposing ZHMF, a zero-shot framework that reformulates the task as natural language question answering using a hierarchical language model with semantic retrieval and reflection, and it outperforms existing models on standard datasets.
Human mobility forecasting is important for applications such as transportation planning, urban management, and personalized recommendations. However, existing methods often fail to generalize to unseen users or locations and struggle to capture dynamic intent due to limited labeled data and the complexity of mobility patterns. We propose ZHMF, a framework for zero-shot human mobility forecasting that combines a semantic enhanced retrieval and reflection mechanism with a hierarchical language model based reasoning system. The task is reformulated as a natural language question answering paradigm. Leveraging LLMs semantic understanding of user histories and context, our approach handles previously unseen prediction scenarios. We further introduce a hierarchical reflection mechanism for iterative reasoning and refinement by decomposing forecasting into an activity level planner and a location level selector, enabling collaborative modeling of long term user intentions and short term contextual preferences. Experiments on standard human mobility datasets show that our approach outperforms existing models. Ablation studies reveal the contribution of each module, and case studies illustrate how the method captures user intentions and adapts to diverse contextual scenarios.