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Disentangling travel characteristics of micro-mobility: Insights provided by an interpretable spatial machine learning modeling framework
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Disentangling travel characteristics of micro-mobility: Insights provided by an interpretable spatial machine learning modeling framework (2026)Disentangling travel characteristics of m…Integration of dockless bike-sharing and metro: Prediction and explanation at origin-destination level (2023)Integration of dockless bike-…Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data (2023)Environmental factors for out…Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis (2022)Understanding cycling distanc…Mode substitution and carbon emission impacts of electric bike sharing systems (2022)Mode substitution and carbon …The rise of the e-bike: Towards an extension of the practice of cycling? 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A Value for n-Person Games (1953)Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: A systematic review (2023)Using machine-learning models to understand nonlinear relationships between land use and travel (2023)Examining active travel behavior through explainable machine learning: Insights from Beijing, China (2024)Using Google Street View to investigate the association between street greenery and physical activity (2018)Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning (2020)To walk or not to walk? 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References

  • 2016Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai299 cited
  • 2021The rise of the e-bike: Towards an extension of the practice of cycling?183 cited
  • 2023Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data177 cited
  • 2010Saturation of Demand for Daily Travel174 cited
  • 2022Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis150 cited
  • 2014China’s One-child Policy and the Changing Family114 cited
  • 2016Age and attitude: Changes in cycling patterns of different e-bike user segments112 cited
  • 2011Day-to-Day Choice to Commute or Not by Bicycle111 cited
  • 2017Differences of Cycling Experiences and Perceptions between E-Bike and Bicycle Users in the United States82 cited
  • 2016Walking, cycling and the urban form: A Heckman selection model of active travel mode and distance by young adolescents80 cited
  • 2022Mode substitution and carbon emission impacts of electric bike sharing systems79 cited
  • 2021Incorporating polycentric development and neighborhood life-circle planning for reducing driving in Beijing: Nonlinear and threshold analysis77 cited
  • 2016Cargo bikes as a growth area for bicycle vs. auto trips: Exploring the potential for mode substitution behavior76 cited

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  • 2019Effects of rural built environment on travel-related CO2 emissions considering travel attitudes73 cited
  • 2023Integration of dockless bike-sharing and metro: Prediction and explanation at origin-destination level67 cited
  • 2019Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data65 cited
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