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Spatiotemporal Heterogeneity Analysis of Influence Factor on Urban Rail Transit Station Ridership
16 references·12 citing·42 second hop
Spatiotemporal Heterogeneity Analysis of Influence Factor on Urban Rail Transit Station Ridership (2021)Spatiotemporal Heterogeneity Analysis of …Spatio-temporal analysis of rail station ridership determinants in the built environment (2018)Spatio-temporal analysis of r…A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership (2018)A geographically and temporal…Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression (2017)Spatial variations in active …Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance (2017)Exploring the influence of bu…Geographical and Temporal Weighted Regression (GTWR) (2015)Geographical and Temporal Wei…Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris (2014)Model-Based Count Series Clus…What influences Metro station ridership in China? 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A Spatial Analysis of Pedestrian Preference (2008)How Far, by Which Route and W…Factors influencing light-rail station boardings in the United States (2004)Factors influencing light-rai…Travel demand and the 3Ds: Density, diversity, and design (1997)Travel demand and the 3Ds: De…Big-City Transit Rider snip, Deficits, and Politics: Avoiding Reality in Boston (1996)Big-City Transit Rider snip, …The influences of built environment on ultra-peak for urban rail transit station passenger flows based on lasso-multiscale geographically weighted regression (2025)The influences of built envir…Quantitative study on the environmental impact of Beijing’s urban rail transit based on carbon emission reduction (2025)Quantitative study on the env…Exploring spatiotemporal dynamic of metro ridership and the influence of built environment factors at the station level: A case study of Nanjing, China (2025)Exploring spatiotemporal dyna…Discovering the nonlinear association between the built environment and metro ridership: Global and local perspectives (2025)Discovering the nonlinear ass…Metro Stations as Catalysts for Land Use Patterns: Evidence from Wuhan Line 11 (2024)Metro Stations as Catalysts f…Applying and Evaluating Data-Driven Fine Grid Partitioning Methods for Traffic Analysis Zones (2024)Applying and Evaluating Data-…Ridership Prediction of Urban Rail Transit Stations Based on AFC and POI Data (2023)Ridership Prediction of Urban…Research of Metro Stations with Varying Patterns of Ridership and Their Relationship with Built Environment, on the Example of Tianjin, China (2023)Research of Metro Stations wi…Spatiotemporal Evolution of Travel Pattern Using Smart Card Data (2022)Spatiotemporal Evolution of T…Measuring the Relationship between Influence Factor and Urban Rail Transit Passenger Flow: Correlation or Causality? 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References

  • 1997Travel demand and the 3Ds: Density, diversity, and design4,588 cited
  • 2010Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices1,501 cited
  • 2015Geographical and Temporal Weighted Regression (GTWR)621 cited
  • 2004Factors influencing light-rail station boardings in the United States443 cited
  • 2008Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas401 cited
  • 2008How Far, by Which Route and Why? A Spatial Analysis of Pedestrian Preference389 cited
  • 2017Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance386 cited
  • 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
  • 2018A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership333 cited
  • 2013What influences Metro station ridership in China? Insights from Nanjing260 cited
  • 2010Factors generating boardings at Metro stations in the Seoul metropolitan area248 cited
  • 2014Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris182 cited
  • 1996Big-City Transit Rider snip, Deficits, and Politics: Avoiding Reality in Boston99 cited

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  • 2010A reliability-based land use and transportation optimization model98 cited
  • 2017Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression81 cited
  • 2018Spatio-temporal analysis of rail station ridership determinants in the built environment72 cited
  • Cited by

    • 2023Research of Metro Stations with Varying Patterns of Ridership and Their Relationship with Built Environment, on the Example of Tianjin, China21 cited
    • 2023Ridership Prediction of Urban Rail Transit Stations Based on AFC and POI Data19 cited
    • 2025Quantitative study on the environmental impact of Beijing’s urban rail transit based on carbon emission reduction18 cited
    • 2022Influence of Built Environment on Subway Trip Origin and Destination: Insights Based on Mobile Positioning Data13 cited
    • 2022Spatiotemporal Evolution of Travel Pattern Using Smart Card Data13 cited
    • 2022Measuring the Relationship between Influence Factor and Urban Rail Transit Passenger Flow: Correlation or Causality?10 cited
    • 2022Analysis of Land Use Optimization of Metro Station Areas Based on Two-Way Balanced Ridership in Xi’an8 cited
    • 2025Exploring spatiotemporal dynamic of metro ridership and the influence of built environment factors at the station level: A case study of Nanjing, China7 cited
    • 2025The influences of built environment on ultra-peak for urban rail transit station passenger flows based on lasso-multiscale geographically weighted regression5 cited
    • 2025Discovering the nonlinear association between the built environment and metro ridership: Global and local perspectives5 cited
    • 2024Applying and Evaluating Data-Driven Fine Grid Partitioning Methods for Traffic Analysis Zones5 cited
    • 2024Metro Stations as Catalysts for Land Use Patterns: Evidence from Wuhan Line 115 cited