Multi-source Spatiotemporal Dataset of Metro Ridership and Station-Area Built Environment for 373 Metro Stations in Beijing (2023)

Description

This dataset takes 373 regularly operated metro stations in Beijing in 2023 as the basic analytical units. It integrates multi-source heterogeneous spatial data including smart card ridership big data from the Automated Fare Collection (AFC) system of rail transit, Amap POIs, OpenStreetMap road networks, Baidu Huiyan population data, real estate transaction records, and urban building vector datasets. A dependent variable system of metro ridership by time periods and 18 independent variables of built environment covering four dimensions are constructed to support the Multiscale Geographically Weighted Regression (MGWR) model for analyzing the multi-scale spatiotemporal heterogeneous driving mechanisms of built environment factors on metro ridership. The dataset fully covers five ridership scenarios: average weekday daily ridership, morning peak inflow/outflow, and evening peak inflow/outflow. It can be applied to empirical research in transport geography and urban-rural planning, such as rail transit demand modeling, transit-oriented development (TOD) station-area planning, jobs-housing separation and commuting analysis, and urban spatial heterogeneity research.

Institutions

• Beijing Jiaotong University

  Beijing, Beijing

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