Human mobility is well described by closed-form gravity-like models learned automatically from data
Abstract Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. However, our understanding and ability to model flows between urban areas are still incomplete. At one end of the modeling spectrum we have gravity models, wh...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56495-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861809013063680 |
|---|---|
| author | Oriol Cabanas-Tirapu Lluís Danús Esteban Moro Marta Sales-Pardo Roger Guimerà |
| author_facet | Oriol Cabanas-Tirapu Lluís Danús Esteban Moro Marta Sales-Pardo Roger Guimerà |
| author_sort | Oriol Cabanas-Tirapu |
| collection | DOAJ |
| description | Abstract Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. However, our understanding and ability to model flows between urban areas are still incomplete. At one end of the modeling spectrum we have gravity models, which are easy to interpret but provide modestly accurate predictions of flows. At the other end, we have machine learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models but do not provide clear insights on human behavior. Here, we show that simple machine-learned, closed-form models of mobility can predict mobility flows as accurately as complex machine learning models, and extrapolate better. Moreover, these models are simple and gravity-like, and can be interpreted similarly to standard gravity models. These models work for different datasets and at different scales, suggesting that they may capture the fundamental universal features of human mobility. |
| format | Article |
| id | doaj-art-6488954c96044d9e8e5d3afc4c14bf53 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-6488954c96044d9e8e5d3afc4c14bf532025-02-09T12:46:09ZengNature PortfolioNature Communications2041-17232025-02-0116111310.1038/s41467-025-56495-5Human mobility is well described by closed-form gravity-like models learned automatically from dataOriol Cabanas-Tirapu0Lluís Danús1Esteban Moro2Marta Sales-Pardo3Roger Guimerà4Department of Chemical Engineering, Universitat Rovira i VirgiliDepartment of Chemical Engineering, Universitat Rovira i VirgiliMedia Lab, Massachusetts Institute of TechnologyDepartment of Chemical Engineering, Universitat Rovira i VirgiliDepartment of Chemical Engineering, Universitat Rovira i VirgiliAbstract Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. However, our understanding and ability to model flows between urban areas are still incomplete. At one end of the modeling spectrum we have gravity models, which are easy to interpret but provide modestly accurate predictions of flows. At the other end, we have machine learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models but do not provide clear insights on human behavior. Here, we show that simple machine-learned, closed-form models of mobility can predict mobility flows as accurately as complex machine learning models, and extrapolate better. Moreover, these models are simple and gravity-like, and can be interpreted similarly to standard gravity models. These models work for different datasets and at different scales, suggesting that they may capture the fundamental universal features of human mobility.https://doi.org/10.1038/s41467-025-56495-5 |
| spellingShingle | Oriol Cabanas-Tirapu Lluís Danús Esteban Moro Marta Sales-Pardo Roger Guimerà Human mobility is well described by closed-form gravity-like models learned automatically from data Nature Communications |
| title | Human mobility is well described by closed-form gravity-like models learned automatically from data |
| title_full | Human mobility is well described by closed-form gravity-like models learned automatically from data |
| title_fullStr | Human mobility is well described by closed-form gravity-like models learned automatically from data |
| title_full_unstemmed | Human mobility is well described by closed-form gravity-like models learned automatically from data |
| title_short | Human mobility is well described by closed-form gravity-like models learned automatically from data |
| title_sort | human mobility is well described by closed form gravity like models learned automatically from data |
| url | https://doi.org/10.1038/s41467-025-56495-5 |
| work_keys_str_mv | AT oriolcabanastirapu humanmobilityiswelldescribedbyclosedformgravitylikemodelslearnedautomaticallyfromdata AT lluisdanus humanmobilityiswelldescribedbyclosedformgravitylikemodelslearnedautomaticallyfromdata AT estebanmoro humanmobilityiswelldescribedbyclosedformgravitylikemodelslearnedautomaticallyfromdata AT martasalespardo humanmobilityiswelldescribedbyclosedformgravitylikemodelslearnedautomaticallyfromdata AT rogerguimera humanmobilityiswelldescribedbyclosedformgravitylikemodelslearnedautomaticallyfromdata |