# GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)

arXiv: https://arxiv.org/abs/2311.05092 · HTML: https://mlsanity.com/paper/2311.05092/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers](https://mlsanity.com/paper/2302.02041/) (2023)
- [Tabular Transformers for Modeling Multivariate Time Series](https://mlsanity.com/paper/2011.01843/) (2020)
- [GEO-BLEU: Similarity Measure for Geospatial Sequences](https://mlsanity.com/paper/2112.07144/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs](https://mlsanity.com/paper/2404.16921/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [Entropy-Driven Curriculum for Multi-Task Training in Human Mobility Prediction](https://mlsanity.com/paper/2509.01613/) (2025)
- [T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobility](https://mlsanity.com/paper/2602.06866/) (2026)
- [Classical Feature Embeddings Help in BERT-Based Human Mobility Prediction](https://mlsanity.com/paper/2510.20275/) (2025)

## Abstract (arXiv)

Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility. Our proposed model is rigorously tested in the context of the HuMob Challenge 2023 -- a competition designed to evaluate the performance of prediction models on standardized datasets to predict human mobility. The challenge leverages two datasets encompassing urban-scale data of 25,000 and 100,000 individuals over a longitudinal period of 75 days. GeoFormer stands out as a top performer in the competition, securing a place in the top-3 ranking. Its success is underscored by performing well on both performance metrics chosen for the competition -- the GEO-BLEU and the Dynamic Time Warping (DTW) measures. The performance of the GeoFormer on the HuMob Challenge 2023 underscores its potential to make substantial contributions to the field of human mobility prediction, with far-reaching implications for disaster preparedness, epidemic control, and beyond.