Block-Fitness Modeling of the Global Air Mobility Network

TL;DR

This paper introduces a generative, maximum-entropy model of the global air transportation network that accurately reproduces its structure and dynamics using limited data, aiding mobility and epidemic modeling.

Contribution

The authors develop a scalable, interpretable stochastic model of the WAN that preserves traffic flows and structural properties, enabling realistic simulations without proprietary data.

Findings

Reproduces key structural properties of the WAN

Accurately simulates dynamic spreading processes

Uses only airport-level passenger flow data

Abstract

Accurate representations of the World Air Transportation Network (WAN) are fundamental inputs to models of global mobility, epidemic risk, and infrastructure planning. However, high-resolution, real-time data on the WAN are largely commercial and proprietary, therefore often inaccessible to the research community. Here we introduce a generative model of the WAN that treats air travel as a stochastic process within a maximum-entropy framework. The model uses airport-level passenger flows to probabilistically generate connections while preserving traffic volumes across geographic regions. The resulting reconstructed networks reproduce key structural properties of the WAN and enable simulations of dynamic spreading that closely match those obtained using the real network. Our approach provides a scalable, interpretable, and computationally efficient framework for forecasting and policy design in global mobility systems.