Summary of Human Mobility Modeling with Limited Information Via Large Language Models, by Yifan Liu et al.
Human Mobility Modeling with Limited Information via Large Language Models
by Yifan Liu, Xishun Liao, Haoxuan Ma, Brian Yueshuai He, Chris Stanford, Jiaqi Ma
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Large Language Model (LLM) empowered human mobility modeling framework that reduces reliance on detailed statistical data. The approach utilizes basic socio-demographic information to generate daily mobility patterns, unlike traditional activity-based models and learning-based algorithms which are limited by dataset availability and quality. Our proposed method leverages semantic information between activities, crucial for modeling interdependencies, and demonstrates strong adaptability across various locations using the NHTS and SCAG-ABM datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict where people will go each day based on simple facts about them, like their age and job. That’s what this paper is all about – creating a new way to model how people move around using language models, which are really good at understanding human behavior. The old methods were stuck because they needed lots of detailed data, but our new approach uses less information and still gets it right! We tested it on real datasets from two places and showed that it works well. |
Keywords
» Artificial intelligence » Large language model
