| Abstrakt: |
In recent years, the explosion of extensive geolocated datasets related to human mobility has presented an opportunity to unravel the mechanism behind daily mobility patterns on an individual and population level; this analysis is essential for solving social matters, such as traffic forecasting, disease spreading, urban planning, and pollution. However, the release of such data is limited owing to the privacy concerns of users from whom data were collected. To overcome this challenge, an innovative approach has been introduced for generating synthetic human mobility, termed as the "Pseudo‐PFLOW" dataset. Our approach leverages open statistical data and a limited travel survey to create a comprehensive synthetic representation of human mobility. The Pseudo‐PFLOW generator comprises three agent models that follow seven fundamental daily activities and captures the spatiotemporal pattern in daily travel behaviors of individuals. The Pseudo‐PFLOW dataset covers the entire population in Japan, approximately 130 million people across 47 prefectures, and has been compared with the existing ground truth dataset. Our generated dataset successfully reconstructs key statistical properties, including hourly population distribution, trip volume, and trip coverage, with coefficient of determination values ranging from 0.5 to 0.98. This innovative approach enables researchers and policymakers to access valuable mobility data while addressing privacy concerns, offering new opportunities for informed decision‐making and analysis. [ABSTRACT FROM AUTHOR] |
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