A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression
Abstract Metro systems provide a mass and sustainable mobility option for urban populations. Drawing on the ridership data in 2019 released by the Shanghai Municipal Transportation Commission, we examine the spatially varying effects of station-level factors measured with different metro-station catchments (MSCs) on the daily and hourly ridership through multiscale geographically weighted regression (MGWR) models. The results indicate that independent variables measured with relatively small and non-overlapping MSCs can better explain the spatial variations in metro ridership. Regarding the important determinants, land use intensity (LUI) demonstrates positive effects diminishing from the core to the peripheral areas, transfer lines (TL) also exhibits positive effects that decrease from southwest to northeast. The effects of road density (RD) and building density (BD) are negative in central urban areas but positive in suburbs, which is the opposite of bus lines (BL). Additionally, the explanatory variables have significantly different ranges of influence, and the range of influence for a single variable also varies across models that use ridership at different times (e.g., weekdays vs. non-weekdays, the morning/evening peak vs. non-peak hours) as dependent variables. This study expands the knowledge of the spatial variations in the relationship between metro ridership and the geographical determinants. The findings can provide empirical evidence and implications for urban planners in formulating context-specific policies to improve the usage of metro systems.
Highlights The effects of station-level factors on metro ridership are examined through MGWR. Different metro-station catchments (MSCs) are considered. Factors measured with small and non-overlapping MSC yields better model fit. The influencing ranges of different variables vary drastically with time. Policies should be sensitive to the geographic contexts of metro stations.
Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression
Abstract Metro systems provide a mass and sustainable mobility option for urban populations. Drawing on the ridership data in 2019 released by the Shanghai Municipal Transportation Commission, we examine the spatially varying effects of station-level factors measured with different metro-station catchments (MSCs) on the daily and hourly ridership through multiscale geographically weighted regression (MGWR) models. The results indicate that independent variables measured with relatively small and non-overlapping MSCs can better explain the spatial variations in metro ridership. Regarding the important determinants, land use intensity (LUI) demonstrates positive effects diminishing from the core to the peripheral areas, transfer lines (TL) also exhibits positive effects that decrease from southwest to northeast. The effects of road density (RD) and building density (BD) are negative in central urban areas but positive in suburbs, which is the opposite of bus lines (BL). Additionally, the explanatory variables have significantly different ranges of influence, and the range of influence for a single variable also varies across models that use ridership at different times (e.g., weekdays vs. non-weekdays, the morning/evening peak vs. non-peak hours) as dependent variables. This study expands the knowledge of the spatial variations in the relationship between metro ridership and the geographical determinants. The findings can provide empirical evidence and implications for urban planners in formulating context-specific policies to improve the usage of metro systems.
Highlights The effects of station-level factors on metro ridership are examined through MGWR. Different metro-station catchments (MSCs) are considered. Factors measured with small and non-overlapping MSC yields better model fit. The influencing ranges of different variables vary drastically with time. Policies should be sensitive to the geographic contexts of metro stations.
Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression
Li, Mengya (author) / Kwan, Mei-Po (author) / Hu, Wenyan (author) / Li, Rui (author) / Wang, Jun (author)
2023-09-28
Article (Journal)
Electronic Resource
English
Transit Ridership Model Based on Geographically Weighted Regression
| British Library Online Contents | 2006
Transit Ridership Model Based on Geographically Weighted Regression
| British Library Conference Proceedings | 2006