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The association between travel demand of docked bike-sharing and the built environment: Evidence from seven US cities

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References

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2015Mining point-of-interest data from social networks for urban land use classification and disaggregation337 cited
2020Built environment effects on the integration of dockless bike-sharing and the metro324 cited
2015Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations275 cited
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2020A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data243 cited
2016How does our natural and built environment affect the use of bicycle sharing?

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215 cited

2017Bicycle sharing system ‘success’ determinants201 cited

2015Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system199 cited

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2025Shared mobility choices in metro connectivity: shared bikes versus shared e-bikes12 cited
2025Comparing built environment effects on bike-sharing and electric bike-sharing usage: a spatiotemporal machine learning approach9 cited
2024Understanding human mobility and trip demand through sparse trajectories of private e-bikes7 cited