Block-fitness modeling of the global air mobility network
Abstract
Accurate representations of origin-destination (OD) passenger flows on the World Air Transportation Network (WAN) are fundamental inputs to models of global mobility, epidemic risk, and infrastructure planning. However, OD data are largely commercial and proprietary, therefore often inaccessible to the research community. Here we introduce a generative model that treats air mobility as a stochastic process within a sparse maximum-entropy framework, informed by aggregate traffic volumes of individual locations and geographic regions. The model accurately reconstructs both network structure and mobility flows, yielding epidemic spreading trajectories that closely match those obtained using real mobility data. Furthermore, the method can be effectively implemented using open data on flight routes and airport traffic, reducing dependence on proprietary datasets. Our approach provides a scalable, interpretable, and computationally efficient framework for scenario analysis and policy design in global mobility systems.
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BibTeX
@article{fischetti2026blockfitness,
title = {{Block-fitness modeling of the global air mobility network}},
author = {Giulia Fischetti and Anna Mancini and Giulio Cimini and Jessica T. Davis and Abby O. K. Leung and Alessandro Vespignani and Guido Caldarelli},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78115-6},
url = {https://www.nature.com/articles/s41467-026-78115-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Block-fitness modeling of the global air mobility network
AU - Giulia Fischetti
AU - Anna Mancini
AU - Giulio Cimini
AU - Jessica T. Davis
AU - Abby O. K. Leung
AU - Alessandro Vespignani
AU - Guido Caldarelli
PY - 2026
DA - 2026/10/12
JO - Nature Communications
DO - 10.1038/s41467-026-78115-6
UR - https://www.nature.com/articles/s41467-026-78115-6
AB - Accurate representations of origin-destination (OD) passenger flows on the World Air Transportation Network (WAN) are fundamental inputs to models of global mobility, epidemic risk, and infrastructure planning. However, OD data are largely commercial and proprietary, therefore often inaccessible to the research community. Here we introduce a generative model that treats air mobility as a stochastic process within a sparse maximum-entropy framework, informed by aggregate traffic volumes of individual locations and geographic regions. The model accurately reconstructs both network structure and mobility flows, yielding epidemic spreading trajectories that closely match those obtained using real mobility data. Furthermore, the method can be effectively implemented using open data on flight routes and airport traffic, reducing dependence on proprietary datasets. Our approach provides a scalable, interpretable, and computationally efficient framework for scenario analysis and policy design in global mobility systems.
SN - 2041-1723
ER -