Metro Ridership and Built Environment Dataset with Streetscape Perception Features (Shanghai)

This dataset integrates passenger flow data from the Shanghai Metro system, multi-source built environment indicators, and streetscape image perception information, aiming to analyze the impact mechanism of the built environment on subway passenger flow under different spatial and temporal contexts. The data covers subway passenger flow during August 2022 and constructs structural, functional, and perceptual built environment features at the station buffer zone scale.

Unlike traditional research that solely relies on structured GIS data, this dataset further incorporates street-view images. By employing deep semantic segmentation techniques, it extracts street spatial perception indicators such as green view ratio, interface enclosure degree, and spatial complexity, providing a more authentic portrayal of the visual environmental experience of pedestrians and passengers around the station. This dataset can be utilized for spatial econometric analysis, interpretable machine learning modeling, and empirical analysis tailored for transportation planning and TOD research.