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  3. A Global Intra-city Commuting Origin-destination Flow Dataset For Urban Sustainable Development.
  1. Home
  3. A Global Intra-city Commuting Origin-destination Flow Dataset For Urban Sustainable Development.

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

Can Rong1,2, Jingtao Ding1,2, Meng Li3

  • 1Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China.

Scientific Data
|June 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a global commuting Origin-Destination (OD) flow dataset, generated using a deep learning model. The dataset captures human mobility patterns and supports sustainable urban development research.

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Area of Science:

  • Urban Science
  • Data Science
  • Transportation Engineering
  • Human Mobility Research

Background:

  • Commuting Origin-Destination (OD) flows are crucial for understanding urban dynamics and sustainable policy-making.
  • Traditional methods for collecting OD flow data are expensive and time-consuming.
  • A need exists for comprehensive, globally representative commuting flow data.

Purpose of the Study:

  • To introduce a novel, globally comprehensive commuting OD flow dataset.
  • To develop a deep generative model for estimating human mobility patterns.
  • To provide a valuable resource for urban science and sustainable development research.

Main Methods:

  • Collected fine-grained demographic data, satellite imagery, and points of interest (POIs) for 2,358 cities worldwide.
  • Employed a deep generative model to capture complex relationships between urban geospatial features and human mobility.
  • Generated commuting OD flows between urban regions based on these relationships.

    • Main Results:

    • A large-scale commuting OD flow dataset with unprecedented global coverage was created.
    • The generated OD flows demonstrated strong alignment with real-world spatial distributions upon validation.
    • The model successfully captured intricate human mobility dynamics across diverse urban environments.

    Conclusions:

    • The developed dataset and methodology offer a cost-effective and efficient alternative to traditional data collection.
    • This resource significantly advances research in urban science, data science, and transportation engineering.
    • The findings support the development of data-driven sustainable urban development strategies.