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Prediction method development for metro station and exit ridership based on the surrounding urban built environment evaluation
16 references·0 citing·45 second hopsampled
Prediction method development for metro station and exit ridership based on the surrounding urban built environment evaluation (2025)Prediction method development for metro s…Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree (2021)Nonlinear and synergistic eff…The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou (2020)The varying patterns of rail …Examining the relationship between built environment and metro ridership at station-to-station level (2020)Examining the relationship be…Understanding the impact of built environment on metro ridership using open source in Shanghai (2019)Understanding the impact of b…Geographical patterns of traffic congestion in growing megacities: Big data analytics from Beijing (2019)Geographical patterns of traf…Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data (2019)Discovering the spatio-tempor…Spatial variations in urban public ridership derived from GPS trajectories and smart card data (2018)Spatial variations in urban p…What determines rail transit passenger volume? Implications for transit oriented development planning (2017)What determines rail transit …Developing a TOD typology for Beijing metro station areas (2016)Developing a TOD typology for…Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul (2015)Land use characteristics of s…What influences Metro station ridership in China? Insights from Nanjing (2013)What influences Metro station…Application of geographically weighted regression to the direct forecasting of transit ridership at station-level (2012)Application of geographically…An analysis of Metro ridership at the station-to-station level in Seoul (2011)An analysis of Metro ridershi…Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea (2010)Transit-oriented development …How to Write Up and Report PLS Analyses (2009)How to Write Up and Report PL…Travel demand and the 3Ds: Density, diversity, and design (1997)Travel demand and the 3Ds: De…Evaluating Structural Equation Models with Unobservable Variables and Measurement Error (1981)Structural Equation Modeling: Concepts, Issues, and Applications. (1996)The partial least squares approach for structural equation modeling. 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References

  • 2009How to Write Up and Report PLS Analyses5,274 cited
  • 1997Travel demand and the 3Ds: Density, diversity, and design4,588 cited
  • 2010Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea420 cited
  • 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
  • 2015Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul320 cited
  • 2020Examining the relationship between built environment and metro ridership at station-to-station level280 cited
  • 2021Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree280 cited
  • 2016Developing a TOD typology for Beijing metro station areas271 cited
  • 2013What influences Metro station ridership in China? Insights from Nanjing260 cited
  • 2018Spatial variations in urban public ridership derived from GPS trajectories and smart card data251 cited
  • 2019Geographical patterns of traffic congestion in growing megacities: Big data analytics from Beijing225 cited
  • 2019Understanding the impact of built environment on metro ridership using open source in Shanghai214 cited
  • 2011An analysis of Metro ridership at the station-to-station level in Seoul197 cited
  • 2019

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Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data
195 cited
  • 2017What determines rail transit passenger volume? Implications for transit oriented development planning169 cited
  • 2020The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou157 cited
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