Spatio-temporal analysis of rail station ridership determinants in the built environment

The development of new routes and stations, as well as changes in land use, can have significant impacts on public transit ridership. Thus, transport departments and governments should seek to determine the level and spatio-temporal dependency of these impacts with the aim of adjusting services or improving planning. However, existing studies primarily focus on predicting ridership, and pay relatively little attention to analyzing the determinants of ridership from temporal and spatial perspectives. Consequently, no comprehensive cognition of the spatio-temporal relationship between station ridership and the built environment can be obtained from previous models, which makes them unable to facilitate the optimization of transportation demands and services. To rectify this problem, we have employed a Bayesian negative binomial regression model to identify the significant impact factors associated with entry/exit ridership at different periods of the day. Based on this model, we formulated geographically weighted models to analyze the spatial dependency of these impacts over different periods. The spatio-temporal relationship between station ridership and the built environment was analyzed using data from Beijing. The results reveal that the temporal impacts of most ridership determinants are related to the passenger trip patterns. Furthermore, the spatial impacts correspond with the determinants’ spatial distribution, and the results give some implications on urban and transportation planning. This analysis gives a common analytical framework analyzing impacts of urban characteristics on ridership, and extending researches on how we capture the impacts of urban and other factors on ridership from a comprehensive perspective.