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Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
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.
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.

