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Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach
16 references·1 citing·46 second hop
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References

  • 2010Travel and the Built Environment5,396 cited
  • 1997Travel demand and the 3Ds: Density, diversity, and design4,588 cited
  • 2002How the built environment affects physical activity1,916 cited
  • 2010Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices1,501 cited
  • 1994Impacts of Mixed Use and Density on Utilization of Three Modes of Travel: Single-Occupant Vehicle, Transit, and Walking903 cited
  • 1977A Geographical Solution to Scale and Aggregation Problems in Region-Building, Partitioning and Spatial Modelling420 cited
  • 2014A new insight into land use classification based on aggregated mobile phone data399 cited
  • 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
  • 2016Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai294 cited
  • 2021Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors156 cited
  • 2022Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis150 cited
  • 2016Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal145 cited
  • 2022A boosting ensemble learning based hybrid light gradient boosting machine and extreme gradient boosting model for predicting house prices

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144 cited
  • 2011Understanding bus rapid transit route ridership drivers: An empirical study of Australian BRT systems116 cited
  • 2022How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis94 cited
  • 2023Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode62 cited
  • Cited by

    • 2024An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment6 cited