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Human Mobility Datasets in the Complex Metro System of Shanghai
Peiyan Sun1,2, Jinming Yang1,2, Zongyuan Huang1,2
1MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Scientific Data
|June 23, 2025
Summary
A new Shanghai metro dataset from 700 million smart card records offers insights into urban mobility. This open-sourced data details passenger flows and types, aiding transportation research.
Area of Science:
- Urban Mobility and Transportation Science
- Data Science and Spatio-Temporal Analysis
Background:
- Metro systems are crucial for urban mobility, necessitating detailed transit data.
- Existing datasets often lack the scale and granularity required for advanced analysis.
Purpose of the Study:
- To construct and release an open-sourced, city-scale metro flow dataset for Shanghai.
- To provide high-resolution data on passenger movements and mobility patterns.
Main Methods:
- Utilized over 700 million smart card records from May-August 2017.
- Processed data to derive in-out station flows and origin-destination (OD) flows.
- Categorized metro flows based on passenger mobility patterns (commuting, home-based-other, none-home-based).
Main Results:
- Developed a comprehensive dataset covering 302 Shanghai metro stations.
- Achieved a 10-minute temporal resolution for flow data.
- Included supplemental metadata: station attributes, network topology, and meteorological records.
Conclusions:
- The dataset provides a novel perspective on urban mobility dynamics.
- Enables advanced research in transportation modeling and spatio-temporal data mining.
- Supports diverse applications in understanding and improving urban transit systems.
