# A Machine Learning Approach to Modeling Human Migration

arXiv: https://arxiv.org/abs/1711.05462 · HTML: https://mlsanity.com/paper/1711.05462/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 15 of 15)

- [Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model](https://mlsanity.com/paper/2306.04873/) (2023)
- [Origin-Destination Network Generation via Gravity-Guided GAN](https://mlsanity.com/paper/2306.03390/) (2023)
- [Harnessing LLMs for Cross-City OD Flow Prediction](https://mlsanity.com/paper/2409.03937/) (2024)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [Generating Origin-Destination Matrices in Neural Spatial Interaction Models](https://mlsanity.com/paper/2410.07352/) (2024)
- [Transportation Scenario Planning with Graph Neural Networks](https://mlsanity.com/paper/2110.13202/) (2021)
- [Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities](https://mlsanity.com/paper/2503.06398/) (2025)
- [Deep learning four decades of human migration](https://mlsanity.com/paper/2506.22821/) (2025)
- [VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns](https://mlsanity.com/paper/2510.02702/) (2025)
- [Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation](https://mlsanity.com/paper/2605.00938/) (2026)
- [DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling](https://mlsanity.com/paper/2606.09086/) (2026)
- [SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2607.05259/) (2026)
- [EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation](https://mlsanity.com/paper/2607.22655/) (2026)
- [Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models](https://mlsanity.com/paper/2608.21567/) (2026)

## Abstract (arXiv)

Human migration is a type of human mobility, where a trip involves a person moving with the intention of changing their home location. Predicting human migration as accurately as possible is important in city planning applications, international trade, spread of infectious diseases, conservation planning, and public policy development. Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only. These models have been validated on commuting flows, a different type of human mobility, and are mainly used in modeling scenarios where large amounts of prior ground truth mobility data are not available. One downside of these models is that they have a fixed form and are therefore not able to capture more complicated migration dynamics. We propose machine learning models that are able to incorporate any number of exogenous features, to predict origin/destination human migration flows. Our machine learning models outperform traditional human mobility models on a variety of evaluation metrics, both in the task of predicting migrations between US counties as well as international migrations. In general, predictive machine learning models of human migration will provide a flexible base with which to model human migration under different what-if conditions, such as potential sea level rise or population growth scenarios.