# Llama-Mob: Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction

arXiv: https://arxiv.org/abs/2410.23692 · HTML: https://mlsanity.com/paper/2410.23692/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [Large Language Models for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2404.17591/) (2024)
- [Context-aware multi-head self-attentional neural network model for next location prediction](https://mlsanity.com/paper/2212.01953/) (2022)
- [Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2310.01224/) (2023)
- [GEO-BLEU: Similarity Measure for Geospatial Sequences](https://mlsanity.com/paper/2112.07144/) (2021)

## Built on by (most cited first, 6 of 6)

- [Importance-Aware Data Selection for Efficient LLM Instruction Tuning](https://mlsanity.com/paper/2511.07074/) (2025)
- [ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework](https://mlsanity.com/paper/2603.07946/) (2026)
- [Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction](https://mlsanity.com/paper/2505.24597/) (2025)
- [Bus-Conditioned Zero-Shot Trajectory Generation via Task Arithmetic](https://mlsanity.com/paper/2602.13071/) (2026)
- [Entropy-Driven Curriculum for Multi-Task Training in Human Mobility Prediction](https://mlsanity.com/paper/2509.01613/) (2025)
- [Classical Feature Embeddings Help in BERT-Based Human Mobility Prediction](https://mlsanity.com/paper/2510.20275/) (2025)

## Abstract (arXiv)

Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting. Traditional methods often rely on designing crafted, domain-specific models, and typically focus on short-term predictions, which struggle to generalize across diverse urban environments. In this study, we introduce Llama3-8B-Mob, a large language model fine-tuned with instruction tuning, for long-term citywide mobility prediction--in a Q&A manner. We validate our approach using large-scale human mobility data from four metropolitan areas in Japan, focusing on predicting individual trajectories over the next 15 days. The results demonstrate that Llama3-8B-Mob excels in modeling long-term human mobility--surpassing the state-of-the-art on multiple prediction metrics. It also displays strong zero-shot generalization capabilities--effectively generalizing to other cities even when fine-tuned only on limited samples from a single city. Moreover, our method is general and can be readily extended to the next POI prediction task. For brevity, we refer to our model as Llama-Mob, and the corresponding results are included in this paper. Source codes are available at https://github.com/TANGHULU6/Llama3-8B-Mob.