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Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study
15 references·0 citing·57 second hopsampled
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References

  • 2015Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto570 cited
  • 2021The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations552 cited
  • 2012Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review416 cited
  • 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challenges261 cited
  • 2022Exploring the causal relationship between urbanization and air pollution: Evidence from China227 cited
  • 2019A systematic review of the effect of infrastructural interventions to promote cycling: strengthening causal inference from observational data116 cited
  • 2021E-bikes and their capability to reduce car CO2 emissions115 cited
  • 2015Investigating the impacts of weather variability on individual’s daily activity–travel patterns: A comparison between commuters and non-commuters in Sweden92 cited
  • 2021Intelligent management of bike sharing in smart cities using machine learning and Internet of Things88 cited
  • 2016Environmental sustainability in container transport: the attitudes of shippers and forwarders73 cited
  • 2021Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review62 cited
  • 2022Causal impacts of the COVID-19 pandemic on daily ridership of public bicycle sharing in Seoul30 cited
  • 2023Examining the causal effects of air pollution on dockless bike-sharing usage using instrumental variables

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29 cited
  • 2024Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks18 cited
  • 2024Forecasting the Usage of Bike-Sharing Systems through Machine Learning Techniques to Foster Sustainable Urban Mobility14 cited
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