SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Exploring the multiscale relationship between the built environment and metro station ridership
16 references·0 citing·40 second hop
Exploring the multiscale relationship between the built environment and metro station ridership (2025)Exploring the multiscale relationship bet…How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis (2022)How the built environment pro…Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai (2021)Spatial heterogeneity in dist…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 …Commuting inequity and its determinants in Shanghai: New findings from big-data analytics (2020)Commuting inequity and its de…Built environment, commuting behaviour and job accessibility in a rail-based dense urban context (2020)Built environment, commuting …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…What determines rail transit passenger volume? Implications for transit oriented development planning (2017)What determines rail transit …Multiscale Geographically Weighted Regression (MGWR) (2017)Multiscale Geographically Wei…Interchange between Metro and Other Modes: Access Distance and Catchment Area (2016)Interchange between Metro and…Application of geographically weighted regression to the direct forecasting of transit ridership at station-level (2012)Application of geographically…Influences of Built Environments on Walking and Cycling: Lessons from Bogotá (2009)Influences of Built Environme…Alternative Approaches to Modeling the Travel-Demand Impacts of Smart Growth (2006)Alternative Approaches to Mod…Factors influencing light-rail station boardings in the United States (2004)Factors influencing light-rai…Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (2002)Geographically Weighted Regre…Travel and the Built Environment: A Synthesis (2001)Travel and the Built Environm…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)Travel demand and the 3Ds: Density, diversity, and design (1997)UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning (2002)Factors generating boardings at Metro stations in the Seoul metropolitan area (2010)What influences Metro station ridership in China? Insights from Nanjing (2013)Spatial Econometrics: Methods and Models (1988)Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity (1996)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)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)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)Annals of the American association of geographers (2016)Inference in Multiscale Geographically Weighted Regression (2019)A Route Map for Successful Applications of Geographically Weighted Regression (2022)Built environments and mode choice: toward a normative framework (2002)Linking urban transport and land use in developing countries (2013)The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou (2020)Transit-oriented development (TOD) typologies around metro station areas in urban China: A comparative analysis of five typical megacities for planning implications (2020)Time-varying and non-linear associations between metro ridership and the built environment (2022)Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis (1998)Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices (2010)Identifying the nonlinear relationship between free-floating bike sharing usage and built environment (2020)Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity (2021)Users of the world, unite! The challenges and opportunities of Social Media (2009)Travel and the Built Environment (2010)Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning (2020)Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors (2021)Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership (2022)How the built environment affects physical activity (2002)AN ECOLOGICAL APPROACH TO CREATING ACTIVE LIVING COMMUNITIES (2005)Infrastructure, programs, and policies to increase bicycling: An international review (2009)Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies (2011)Shaping cities for health: complexity and the planning of urban environments in the 21st century (2012)Methods to account for spatial autocorrelation in the analysis of species distributional data: a review (2007)A measure of spatial stratified heterogeneity (2016)Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2016)
this work references cited by second hop

References

  • 2002Geographically Weighted Regression: The Analysis of Spatially Varying Relationships3,946 cited
  • 2001Travel and the Built Environment: A Synthesis1,882 cited
  • 2017Multiscale Geographically Weighted Regression (MGWR)1,381 cited
  • 2009Influences of Built Environments on Walking and Cycling: Lessons from Bogotá832 cited
  • 2004Factors influencing light-rail station boardings in the United States443 cited
  • 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
  • 2006Alternative Approaches to Modeling the Travel-Demand Impacts of Smart Growth263 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 data195 cited
  • 2017What determines rail transit passenger volume? Implications for transit oriented development planning169 cited
  • 2021Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai98 cited
  • 2022How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis94 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

2020
Commuting inequity and its determinants in Shanghai: New findings from big-data analytics
77 cited
  • 2020Built environment, commuting behaviour and job accessibility in a rail-based dense urban context64 cited
  • 2016Interchange between Metro and Other Modes: Access Distance and Catchment Area62 cited
  • Cited by

    No citing works inside the corpus for this window.