ArticleNature communications2025
Human mobility is well described by closed-form gravity-like models learned automatically from data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning forecasts suggest a concentration paradox in international student mobility to the United Kingdom.Nature communications · 2026Article
- Deep learning four decades of human migration.Nature · 2026Article
- Uncovering latent urban mobility patterns via smart-card and survey data fusion.Nature communications · 2026Article
- A Global Intra-city Commuting Origin-Destination Flow Dataset for Urban Sustainable Development.Scientific data · 2026Article
- Distance-amplified power-law distributions better characterize human long-distance travel.Scientific reports · 2026Article
- Mountain moves: Spatial interaction modelling of Bulgaria's internal migration (1934-1992).PloS one · 2026Article
- Modeling resource consumption in the US air transportation system via minimum-cost percolation.Nature communications · 2025Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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