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Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen
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Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen (2024)Estimating Rail Transit Passenger Flow Co…Time-varying and non-linear associations between metro ridership and the built environment (2022)Time-varying and non-linear a…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 …Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China (2020)Spatially varying impacts of …Understanding the impact of built environment on metro ridership using open source in Shanghai (2019)Understanding the impact of b…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…A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership (2018)A geographically and temporal…Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul (2015)Land use characteristics of s…Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014)Geographically weighted regre…Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership (2014)Exploring the impacts of land…Application of geographically weighted regression to the direct forecasting of transit ridership at station-level (2012)Application of geographically…Rail-based transit-oriented development: Lessons from New York City and Hong Kong (2010)Rail-based transit-oriented d…Factors generating boardings at Metro stations in the Seoul metropolitan area (2010)Factors generating boardings …The Role of Land Use in Travel Mode Choice: Evidence from Boston and Hong Kong (2004)The Role of Land Use in Trave…Factors influencing light-rail station boardings in the United States (2004)Factors influencing light-rai…Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis (1998)Geographically Weighted Regre…Impacts of Mixed Use and Density on Utilization of Three Modes of Travel: Single-Occupant Vehicle, Transit, and Walking (1994)A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area (1997)Travel demand and the 3Ds: Density, diversity, and design (1997)How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds (2019)Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree (2021)The Geography of Urban Transportation (1988)Transit ridership forecasting at station level: an approach based on distance-decay weighted regression (2011)New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas (2013)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)The Analysis of Spatial Association by Use of Distance Statistics (1992)Local Indicators of Spatial Association—LISA (1995)The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models (2014)Geographically weighted regression with parameter-specific distance metrics (2016)Spatial effects of accessibility to parks on housing prices in Shenzhen, China (2017)Spatial Econometrics: Methods and Models (1988)Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity (1996)Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (2002)The role of distance in peri-urban national park use: Who visits them and how far do they travel? (2015)Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China (2016)Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations (2018)Travel and the Built Environment: A Synthesis (2001)Correlation or causality between the built environment and travel behavior? Evidence from Northern California (2005)Advance transit oriented development typology: case study in Brisbane, Australia (2013)Lisrel 8: Structural Equation Modeling With the Simplis Command Language (1993)Principles and Practice of Structural Equation Modeling (1998)Principles and Practice of Structural Equation Modeling (2012)Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning (2020)Sustainability and Cities: Overcoming Automobile Dependence (1999)Role of the built environment on mode choice decisions: additional evidence on the impact of density (2007)Human Settlements, Infrastructure and Spatial Planning (2014)Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance (2017)Spatial Processes, Models and Applications (1983)Under the hood Issues in the specification and interpretation of spatial regression models (2002)Under the hood Issues in the specification and interpretation of spatial regression models (2002)Multicollinearity and correlation among local regression coefficients in geographically weighted regression (2005)Activity-based disaggregate travel demand model system with activity schedules (2001)Crowding valuation in urban tram and bus transportation based on smart card data (2018)Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system (2019)Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices (2018)A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data (2020)
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References

  • 1998Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis1,154 cited
  • 2004Factors influencing light-rail station boardings in the United States443 cited
  • 2014Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data403 cited
  • 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
  • 2010Rail-based transit-oriented development: Lessons from New York City and Hong Kong369 cited
  • 2004The Role of Land Use in Travel Mode Choice: Evidence from Boston and Hong Kong364 cited
  • 2018A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership333 cited
  • 2015Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul320 cited
  • 2018Spatial variations in urban public ridership derived from GPS trajectories and smart card data251 cited
  • 2010Factors generating boardings at Metro stations in the Seoul metropolitan area248 cited
  • 2020Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China225 cited
  • 2019Understanding the impact of built environment on metro ridership using open source in Shanghai214 cited
  • 2019Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data

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195 cited
  • 2022Time-varying and non-linear associations between metro ridership and the built environment185 cited
  • 2014Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership182 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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