LMP: Large Language Model Enhanced Intent-aware Mobility Prediction

ACL ARR 2025 May Submission6735 Authors

Abstract: Human mobility prediction is essential for applications like urban planning and transportation management, yet it remains challenging due to the complex, often implicit, intentions behind human behavior. Existing models predominantly focus on spatiotemporal patterns, paying less attention to the underlying intentions that govern movements. Recent advancements in large language models (LLMs) offer a promising alternative research angle for integrating commonsense reasoning into mobility prediction. However, it is a non-trivial problem because LLMs are not natively built for mobility intention inference, and they also face scalability issues and integration difficulties with spatiotemporal models. To address these challenges, we propose a novel LMP (LLMs for Mobility Prediction) framework. Specifically, LMP introduces an Schema Learning-based Agentic Workflow to unleash LLM’s commonsense reasoning power for mobility intention inference. Besides, we design a data-efficient intent fine-tuning scheme to effectively distilling reasoning power from proprietary LLM to smaller-scale, open-source language model, ensuring LMP’s scalability to millions of mobility records. Moreover, we propose a transformer-based intent-aware mobility prediction model to effectively harness the intent inference ability of LLM. Evaluated on three real-world datasets, LMP outperforms state-of-the-art baselines on 11 out of 12 metrics and ranks as the second-best method on the remaining one, demonstrating improved accuracy in next-location prediction and effective intention inference. The interpretability of intention-aware mobility prediction highlights our LMP framework’s potential for real-world applications.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: human behavior analysis; event extraction; multimodal applications; NLP tools for social analysis; applications

Languages Studied: English, Chinese

Submission Number: 6735