Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns

Although several studies have explored the impacts of built environment factors on rail transit station ridership, the spatiotemporal heterogeneity of the effects and different ridership patterns have rarely been considered. This study explored different ridership patterns and considered the spatiotemporal heterogeneity of the effects. Taking Beijing, China, as an example, the built environment factors were precisely characterized by integrating multi-source big data, including mobile phone, point of interest (POI), housing prices, and road network data. Three direct ridership models (DRMs) based on the geographically and temporally weighted regression (GTWR) model were proposed to reveal the spatiotemporal effects of built environment factors on daily, weekday hourly, and weekend hourly ridership patterns. The results showed that population density and employment density are the dominant factors affecting commuting ridership; however, the spatial variations of their effects exhibit significantly different distributions. Entertainment places are highly positively related to weekend ridership, and the strongest coefficients are mainly concentrated in the southwestern urban center and suburban residential areas. The results also highlighted the importance of bus stations and parking lots in attracting commuting ridership. These findings can provide insights into urban rail transit planning and facilitate the coordinated development of urban public transportation systems and land use.