A Global Commuting Origin-Destination Flow Dataset for Urban Sustainable Development

TL;DR

This paper presents a comprehensive global dataset of commuting origin-destination flows for 1,625 cities, generated using a deep generative model that leverages demographic, satellite, and POI data to support sustainable urban development research.

Contribution

The study introduces a novel, large-scale global commuting OD flow dataset and a deep generative modeling approach to estimate urban mobility patterns across diverse cities.

Findings

Generated flows closely match real-world observations.

Dataset covers 6 continents and 179 countries.

Deep model effectively captures complex urban mobility relationships.

Abstract

Commuting Origin-Destination (OD) flows capture movements of people from residences to workplaces, representing the predominant form of intra-city mobility and serving as a critical reference for understanding urban dynamics and supporting sustainable policies. However, acquiring such data requires costly, time-consuming censuses. In this study, we introduce a commuting OD flow dataset for cities around the world, spanning 6 continents, 179 countries, and 1,625 cities, providing unprecedented coverage of dynamics under diverse urban environments. Specifically, we collected fine-grained demographic data, satellite imagery, and points of interest~(POIs) for each city as foundational inputs to characterize the functional roles of urban regions. Leveraging these, a deep generative model is employed to capture the complex relationships between urban geospatial features and human mobility, enabling the generation of commuting OD flows between urban regions. Comprehensively, validation shows that the spatial distributions of the generated flows closely align with real-world observations. We believe this dataset offers a valuable resource for advancing sustainable urban development research in urban science, data science, transportation engineering, and related fields.