Human Mobility Datasets in the Complex Metro System of Shanghai

Abstract The growing role of metro systems in urban mobility calls for high-quality metro transit datasets. Derived from over 700 million smart card records, an open-sourced, city-scale metro flow dataset was constructed, covering the period of May-August 2017 and 302 metro stations in Shanghai, Chi...

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Main Authors: Peiyan Sun, Jinming Yang, Zongyuan Huang, Shaoyu Huang, Shengyuan Xu, Weipeng Wang, Wenxuan Guo, Yuting Feng, Xi Zhai, Tao Yang, Xiaokang Yang, Yaohui Jin, Yanyan Xu
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-05416-8
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Summary:Abstract The growing role of metro systems in urban mobility calls for high-quality metro transit datasets. Derived from over 700 million smart card records, an open-sourced, city-scale metro flow dataset was constructed, covering the period of May-August 2017 and 302 metro stations in Shanghai, China. The in-out flow counts of each station and OD flow between stations were offered at a 10-minute temporal resolution. By leveraging the mobility patterns of each passenger, metro flows were categorized into commuting flows, home-based-other flows, and none-home-based flows, providing a more comprehensive perspective towards urban mobility dynamics. Supplemental metadata, including station attributes, network topology, and meteorological records further support potential applications. This city-scale metro flow dataset could be utilized in advancing research in transportation modeling, spatio-temporal data mining, and urban mobility analysis.
ISSN:2052-4463