Abstract
Ridership analysis at the local level has a pivotal role in sustainable urban construction and transportation planning. In practice, urban rail transit (URT) ridership is affected by complex factors that vary across the urban area. The aim of this study is to model and explore the factors that impact metro station ridership in Shenzhen, China from a local perspective. The direct demand model, which uses ordinary least squares (OLS) estimation, is the most widely used method of ridership modeling. However, OLS estimation assumes parametric stability. This study investigates the use of a direct demand model on the basis of geographically weighted regression (GWR) to model the local relationships between metro station ridership and potential influencing factors. Real-world Shenzhen Metro smart card data are used to test and verify the applicability and performance of the model. The results show that GWR performs better than OLS estimation in terms of both model fitting and spatial interpretation. The GWR model demonstrates a high level of interpretability regarding the spatial distribution and variation of each coefficient, and thus can provide insights for decision-makers into URT ridership and its complex factors from a local perspective.