[Objective] The ridership characteristics of urban rail transit stations are closely related to the surrounding built environment and socio-economic factors, and the influence of different influencing factors on ridership characteristics also has temporal and spatial heterogeneity. Considering the complexity of influencing factors on station ridership, this paper uses the multiscale geographical weighted regression (MGWR) model to analyze the influencing factors of ridership at rail transit stations in different temporal scales.[Methods] This paper selects the station ridership on weekdays as the dependent variable, which is divided into five categories, including the average daily ridership, inbound ridership of morning peak hours, outbound ridership of morning peak hours, inbound ridership of evening peak hours, and outbound ridership of evening peak hours. A total of 23 independent variables are selected from three aspects: station attributes, connectivity, and the built environment. The variance inflation factor and Moran index are utilized to test the linear correlation and spatial autocorrelation between independent variables, respectively. The MGWR model is applied to construct the analysis model of ridership characteristics, and three indicators, including the residual sum of squares (RSS), adjusted R2, and the corrected Akaike information criterion (AICc), are employed to compare the performance of the ordinary least squares (OLS), geographically weighted regression (GWR), and MGWR models. The influencing factors and their interaction with rail transit station ridership in different temporal scales are developed. Finally, this method is applied to analyze the influence degree of ridership characteristics at Nanjing rail transit station. [Results] The following results are presented. 1) The MGWR model is more reliable than the OLS and GWR models. 2) The average daily ridership analysis model, which ignores the impact of morning and evening peak hour ridership, has the most significant independent variables. 3) The distance to the city center has a significant negative impact on station ridership, indicating that the agglomeration of station ridership is evident when the station is close to the city center. 4) The stations with a high proportion of residential and living facilities have a strong attraction to the morning peak inbound and evening peak outbound ridership, whereas those with a low proportion of residential and livings facilities have a strong attraction to the morning peak outbound and evening peak inbound ridership. Three significant local variables, namely tourism facility POI density, enterprise and office POI density, and the ratio of floor area on commercial lands to the total floor area, are available, and these local variables have different impacts on rail transit ridership at different temporal scales. Tourism facility POI density has negative spatially varying impacts on the average daily ridership, inbound ridership of morning peak hours, and outbound ridership of evening peak hours. Enterprise and office POI density has a negative spatially varying impact on inbound ridership of morning peak hours but has a positive spatially varying impact on outbound ridership of morning peak hours. The ratio of floor area on commercial lands to the total floor area has positive and negative spatially varying impacts on inbound ridership during evening peak hours. This finding implies that not all the commercial buildings around the rail transit stations are attractive to the inbound ridership during evening peak hours.[Conclusions] The MGWR model considering spatial autocorrelation can capture numerous influence scales of different variables and reduce the deviation of results. The developed method in this paper achieves the expected goal and depicts the interdependence between ridership and influencing factors from the station level. © 2023 Press of Tsinghua University. All rights reserved.
