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Exploring Nonlinear and Interactive Influences of Built Environment on Metro‐To‐Bus Transfer Behavior
16 references·0 citing·40 second hop
Exploring Nonlinear and Interactive Influences of Built Environment on Metro‐To‐Bus Transfer Behavior (2025)Exploring Nonlinear and Interactive Influ…Time-varying and non-linear associations between metro ridership and the built environment (2022)Time-varying and non-linear a…Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity (2022)Nonlinear effects of factors …Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors (2021)Understanding the modifiable …Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity (2021)Nonlinear effects of built en…Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach (2021)Non-linear associations betwe…Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data (2020)Taxi hailing choice behavior …Examining non-linear built environment effects on elderly’s walking: A random forest approach (2020)Examining non-linear built en…Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data (2019)Discovering the spatio-tempor…Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning (2019)Bike-sharing or taxi? Modelin…Applying a random forest method approach to model travel mode choice behavior (2018)Applying a random forest meth…A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership (2018)A geographically and temporal…A comparative study of machine learning classifiers for modeling travel mode choice (2017)A comparative study of machin…An analysis of Metro ridership at the station-to-station level in Seoul (2011)An analysis of Metro ridershi…Influences of Built Environments on Walking and Cycling: Lessons from Bogotá (2009)Influences of Built Environme…Access to railway stations and its potential in increasing rail use (2008)Access to railway stations an…Travel demand and the 3Ds: Density, diversity, and design (1997)Travel demand and the 3Ds: De…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)Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures (2003)Multiple Factor Analysis (2007)WHITE PAPER, European transport policy for 2010 : time to decide (2004)Walking and Cycling for Healthy Cities (2010)Perceived accessibility, mobility, and connectivity of public transportation systems (2015)Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution (2016)Principles and Practice of Structural Equation Modeling (1998)A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels (2006)Transit-oriented development (TOD) typologies around metro station areas in urban China: A comparative analysis of five typical megacities for planning implications (2020)Conditional logit analysis of qualitative choice behavior (1972)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Travel and the Built Environment (2010)Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis (2018)Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020)Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity (1996)Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (2002)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)Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning (2020)On the measurement of inequality (1970)Built environments and mode choice: toward a normative framework (2002)The effects of attitudes and personality traits on mode choice (2005)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)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)How derived is the demand for travel? Some conceptual and measurement considerations (2001)Travel and the Built Environment: A Synthesis (2001)Model-Dependent Variance Inflation Factor Cutoff Values (2002)Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree (2021)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)COVID-19 effects on property markets: The pandemic decreases the implicit price of metro accessibility (2022)Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis (2022)Using machine-learning models to understand nonlinear relationships between land use and travel (2023)
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References

  • 1997Travel demand and the 3Ds: Density, diversity, and design4,588 cited
  • 2009Influences of Built Environments on Walking and Cycling: Lessons from Bogotá832 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
  • 2008Access to railway stations and its potential in increasing rail use374 cited
  • 2020Examining non-linear built environment effects on elderly’s walking: A random forest approach352 cited
  • 2018A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership333 cited
  • 2021Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach250 cited
  • 2011An analysis of Metro ridership at the station-to-station level in Seoul197 cited
  • 2019Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data195 cited
  • 2022Time-varying and non-linear associations between metro ridership and the built environment185 cited
  • 2021Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors156 cited
  • 2021Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity

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145 cited
  • 2019Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning93 cited
  • 2022Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity89 cited
  • 2020Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data87 cited
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