A Foundational individual Mobility Prediction Model based on Open-Source Large Language Models
This work addresses the challenge of adapting mobility prediction models to different cities and users with diverse contexts, which is an incremental improvement over existing LLM-based methods.
The paper tackled the problem of individual mobility prediction by proposing a unified fine-tuning framework for open-source large language models, achieving the best performance in prediction accuracy and transferability across six real-world datasets compared to state-of-the-art models.
Large Language Models (LLMs) are widely applied to domain-specific tasks due to their massive general knowledge and remarkable inference capacities. Current studies on LLMs have shown immense potential in applying LLMs to model individual mobility prediction problems. However, most LLM-based mobility prediction models only train on specific datasets or use single well-designed prompts, leading to difficulty in adapting to different cities and users with diverse contexts. To fill these gaps, this paper proposes a unified fine-tuning framework to train a foundational open source LLM-based mobility prediction model. We conducted extensive experiments on six real-world mobility datasets to validate the proposed model. The results showed that the proposed model achieved the best performance in prediction accuracy and transferability over state-of-the-art models based on deep learning and LLMs.