Unraveling Spatial–Temporal and Interactive Impact of Built Environment on Metro Ridership: A Case Study in Shanghai, China

Qingwen Xue, Lingzhi Cheng, Zhichao Li, Yingying Xing, Hongwei Wang, Hongwei Li and Yichuan Peng ()
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Qingwen Xue: College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Lingzhi Cheng: The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
Zhichao Li: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Yingying Xing: The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
Hongwei Wang: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore 138632, Singapore
Hongwei Li: College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing 210098, China
Yichuan Peng: The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China

Sustainability, 2025, vol. 17, issue 21, 1-20

Abstract: Urban rail transit, as a green, environmentally friendly, safe, and efficient mode of transportation, plays a crucial role in urban sustainable development. However, the influencing mechanism of build environment factors on rail transit ridership still needs to be further investigated. Also, the interaction effects between these factors have not been considered. This study aims to explore the relationship and impact of built environmental factors on metro ridership. The research employs the Multiscale Geographically Weighted Regression (MGWR) model to analyze the temporal and spatial effects of built environmental factors on the rail transit ridership. The GeoDetector model is utilized to investigate the interactive effects of these factors on rail transit ridership. The Shanghai Metro ridership data and built environment data are applied to validate the model. Based on data analysis results, we found that Food & Beverages and Accommodation services, respectively, have the greatest impact on metro ridership on weekdays and weekends. Furthermore, the interaction effects between other variable and Land use diversity significantly enhance rail transit ridership, validating the promoting effect of land use diversity on metro ridership. By proposing recommendations for relevant urban planning and policy formulation, we can foster the sustainable development of urban rail transit.

Keywords: metro ridership; built environment factors; Multiscale Geographically Weighted Regression (MGWR); GeoDetector (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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