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

2025-05-21

AI Summary

Commuter origin-destination (OD) flow data are critical for urban sustainability research, yet conventional census-based approaches suffer from high costs and limited spatial coverage. To address this, we propose the first global, multi-source, heterogeneous-data-driven generative OD modeling framework, covering 1,625 cities across 179 countries on six continents. Our method integrates fine-grained demographic statistics, satellite imagery, and point-of-interest (POI) data, leveraging deep generative models to learn the nonlinear mapping between urban spatial structure and commuting behavior—enabling cross-regional, multi-scale, and highly generalizable city-level OD flow generation. The generated OD flows exhibit strong agreement with ground-truth observations (mean Pearson correlation coefficient > 0.89). This work delivers the first globally scalable, reproducible, and low-cost standardized commuter flow dataset, significantly bridging a long-standing gap in urban mobility and sustainability research.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Lack of global commuting OD flow data for urban studies
• High cost and time required for traditional census methods
• Need for accurate urban mobility modeling across diverse cities

Innovation

• Global commuting OD flow dataset creation
• Deep generative model for mobility prediction
• Integration of demographic and geospatial data