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Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing
16 references·3 citing·37 second hop
Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing (2024)Built Environment Renewal Strategies Aime…Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020)Prediction and behavioral ana…Examining the relationship between built environment and metro ridership at station-to-station level (2020)Examining the relationship be…Inference in Multiscale Geographically Weighted Regression (2019)Inference in Multiscale Geogr…How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds (2019)How does the station-area bui…Applying a random forest method approach to model travel mode choice behavior (2018)Applying a random forest meth…Multiscale Geographically Weighted Regression (MGWR) (2017)Multiscale Geographically Wei…A comparative study of machine learning classifiers for modeling travel mode choice (2017)A comparative study of machin…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…Half-Mile Circle (2012)Half-Mile CircleApplication of geographically weighted regression to the direct forecasting of transit ridership at station-level (2012)Application of geographically…Transit ridership forecasting at station level: an approach based on distance-decay weighted regression (2011)Transit ridership forecasting…Travel and the Built Environment (2010)Travel and the Built Environm…Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea (2010)Transit-oriented development …Rail-based transit-oriented development: Lessons from New York City and Hong Kong (2010)Rail-based transit-oriented d…Suburbanization and transit-oriented development in China (2008)Suburbanization and transit-o…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…Applying machine learning to decode built environment thresholds for public and active transport distances in the global south (2025)Applying machine learning to …Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach (2024)Investigating the Nonlinear E…How Accessibility Shapes Land Use (1959)Travel demand and the 3Ds: Density, diversity, and design (1997)Built environments and mode choice: toward a normative framework (2002)A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership (2018)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)Mixed land-uses and commuting: Evidence from the American Housing Survey (1996)Statistical Methods for Meta-Analysis (1988)Introduction to Meta‐Analysis (2009)Role of Built Environments in Physical Activity, Obesity, and Cardiovascular Disease (2012)City planning and population health: a global challenge (2016)Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities (2021)Introduction to Meta‐Analysis (2021)Conditional logit analysis of qualitative choice behavior (1972)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis (2018)Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach (2021)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)Annals of the American association of geographers (2016)A Route Map for Successful Applications of Geographically Weighted Regression (2022)On the measurement of inequality (1970)The effects of attitudes and personality traits on mode choice (2005)Examining non-linear built environment effects on elderly’s walking: A random forest approach (2020)To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults (2021)Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis (1998)Computational improvements to multi-scale geographically weighted regression (2020)Exploring differentiated impacts of socioeconomic factors and urban forms on city-level CO2 emissions in China: Spatial heterogeneity and varying importance levels (2022)Multiscale GIS based-model to assess urban social vulnerability and associated risk: Evidence from 146 urban centers of Eastern India (2023)Mixed MNL models for discrete response (2000)Predicting the travel mode choice with interpretable machine learning techniques: A comparative study (2022)
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References

  • 2010Travel and the Built Environment5,396 cited
  • 2017Multiscale Geographically Weighted Regression (MGWR)1,381 cited
  • 2018Applying a random forest method approach to model travel mode choice behavior491 cited
  • 2017A comparative study of machine learning classifiers for modeling travel mode choice423 cited
  • 2010Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea420 cited
  • 2019Inference in Multiscale Geographically Weighted Regression414 cited
  • 2020Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models406 cited
  • 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
  • 2011Transit ridership forecasting at station level: an approach based on distance-decay weighted regression371 cited
  • 2010Rail-based transit-oriented development: Lessons from New York City and Hong Kong369 cited
  • 2012Half-Mile Circle337 cited
  • 2008Suburbanization and transit-oriented development in China334 cited
  • 2015Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul320 cited
  • 2019How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds

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311 cited
  • 2020Examining the relationship between built environment and metro ridership at station-to-station level280 cited
  • 2013What influences Metro station ridership in China? Insights from Nanjing260 cited
  • Cited by

    • 2024Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach8 cited
    • 2025The influences of built environment on ultra-peak for urban rail transit station passenger flows based on lasso-multiscale geographically weighted regression5 cited
    • 2025Applying machine learning to decode built environment thresholds for public and active transport distances in the global south5 cited