# Human mobility is well described by closed-form gravity-like models learned automatically from data

arXiv: https://arxiv.org/abs/2312.11281 · HTML: https://mlsanity.com/paper/2312.11281/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [A Bayesian machine scientist to aid in the solution of challenging scientific problems](https://mlsanity.com/paper/2004.12157/) (2020)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [Fundamental limits to learning closed-form mathematical models from data](https://mlsanity.com/paper/2204.02704/) (2022)
- [Reconstructing commuters network using machine learning and urban indicators](https://mlsanity.com/paper/1908.03512/) (2019)
- [Consistencies and inconsistencies between model selection and link prediction in networks](https://mlsanity.com/paper/1705.07967/) (2017)
- [Limits of Predictability in Commuting Flows in the Absence of Data for Calibration](https://mlsanity.com/paper/1407.6256/) (2014)
- [FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation](https://mlsanity.com/paper/2307.11214/) (2023)

## Built on by (most cited first, 11 of 11)

- [MobilityGPT: Enhanced Human Mobility Modeling with a GPT model](https://mlsanity.com/paper/2402.03264/) (2024)
- [Transferable Human Mobility Network Reconstruction with neuroGravity](https://mlsanity.com/paper/2604.23678/) (2026)
- [Eyes Will Shut: A Vision-Based Next GPS Location Prediction Model by Reinforcement Learning from Visual Map Feed Back](https://mlsanity.com/paper/2507.18661/) (2025)
- [Deep learning four decades of human migration](https://mlsanity.com/paper/2506.22821/) (2025)
- [Efficient Temporal Tokenization for Mobility Prediction with Large Language Models](https://mlsanity.com/paper/2507.14017/) (2025)
- [Bayesian symbolic regression: Automated equation discovery from a physicists' perspective](https://mlsanity.com/paper/2507.19540/) (2025)
- [Deep Generative Model for Human Mobility Behavior](https://mlsanity.com/paper/2510.06473/) (2025)
- [Predicting Healthcare System Visitation Flow by Integrating Hospital Attributes and Population Socioeconomics with Human Mobility Data](https://mlsanity.com/paper/2601.15977/) (2026)
- [Latent patterns of urban mixing in mobility analysis across five global cities](https://mlsanity.com/paper/2604.12202/) (2026)
- [Autonomous discovery of traffic laws with AI traffic scientists](https://mlsanity.com/paper/2607.01639/) (2026)
- [Inferring Urban Mobility Interactions from Aggregated Dynamics](https://mlsanity.com/paper/2609.07349/) (2026)

## Abstract (arXiv)

Modeling of human mobility is critical to address questions in urban planning and transportation, as well as global challenges in sustainability, public health, and economic development. However, our understanding and ability to model mobility flows within and between urban areas are still incomplete. At one end of the modeling spectrum we have simple so-called gravity models, which are easy to interpret and provide modestly accurate predictions of mobility flows. At the other end, we have complex machine learning and deep learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models at the cost of not being interpretable and not providing insight on human behavior. Here, we show that simple machine-learned, closed-form models of mobility are able to predict mobility flows more accurately, overall, than either gravity or complex machine and deep learning models. At the same time, these models are simple and gravity-like, and can be interpreted in terms similar to standard gravity models. Furthermore, these models work for different datasets and at different scales, suggesting that they may capture the fundamental universal features of human mobility.