关键词: 城市交通, 通勤乘车率, 多尺度地理加权回归, 城市轨道交通, 空间异质性

Abstract: There is a significant relationship between the built environment and the characteristics of urban rail transit ridership. However, existing studies mainly focus on the impact of built environment on passenger flow of urban rail transit stations, and rarely analyze the commuter ridership rate of stations. Station passenger flow reflects the transportation intensity of urban rail transit, while station commuter ridership rate reflects the sharing capacity of urban rail transit, both of which have an important impact on the operational effectiveness of passenger flow. This study proposed a method to measure the commuter ridership rate of urban rail transit stations based on urban rail transit smart card data and cell phone signaling data, and utilized a multi-scale geographically weighted regression (MGWR) model to investigate and compare the differences in the impact of the built environment on the passenger flow and commuter ridership rate of the stations. The study case in Tianjin of China indicated that: the passenger flow of urban rail transit stations showed a decreasing spatial distribution from the center of the city to the suburbs, while the commuter ridership rate showed an increasing spatial distribution from the center of the city to the suburbs. There were both significant differences and similarities in the built environment factors affecting station passenger flow and commuter ridership rate, with the average distance of a station from a transit stop being the global variable affecting both and having a significant negative effect. There existed spatial heterogeneity in the intensity and direction of the effect of local influence variables in the built environment on station passenger flow and commuter ridership rate. The differences in the effects of the built environment on station passenger flow and commuter ridership rate indicated that it was important to consider not only the differences in the types of built environment factors contributing to these two different indicators of passenger flow characteristics, but also the differences in the spatial effects of localized influences on the built environment. In the future planning, the classification of synergistic configuration, zoning level intervention of differentiated strategies, integrated activation of the built environment factors of the heterogeneous effect should be considered to comprehensively enhance the station passenger flow operational efficiency.

Key words: urban traffic, commuter ridership rate, multiscale geographically weighted regression, urban rail transit, spatial heterogeneity