Abstract
Although prior literature has examined the impact of the built environment on cycling behavior, the focus has been confined to macro-level environmental characteristics or limited objective features. The role of perceived qualities measured from visual surveys is largely unknown. Using a large amount of dockless bikeshare trajectories, this study maps the cycling trip volume at the street segment level. The research evaluates the micro-level objective features and perceived qualities along the cycling routes using street view imagery, computer vision, and machine learning. Through several regression models, the strengths of both micro-level environment characteristic groups are comprehensively analyzed to reveal their impacts on cycling volume at the street level. Overall, objective features exhibit higher predictive power than perceived qualities, while perceived qualities can complement objective features. The research justifies the significant impacts of micro-level environment characteristics on cycling route choices. It provides a valuable reference for urban planning toward a sustainable cycling-friendly city.
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Acknowledgements
The authors would like to thank Alan Black Transportation Related Grant (2020, 2021) from the Department of City and Regional Planning and the Serve in Place Fund (2020, 2021) from the Office of Engagement Initiatives at Cornell University for their support. The authors would also like to thank the City of Ithaca, Hector Chang, Bike Walk Tompkins, and Lime for providing secondary bikeshare trip data. This research could not have been made without the support from Bike Walk Tompkins.
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Song, Q., Li, W., Li, J., Wei, X., Qiu, W. (2023). Disclosing the Impact of Micro-level Environmental Characteristics on Dockless Bikeshare Trip Volume: A Case Study of Ithaca. In: Goodspeed, R., Sengupta, R., Kyttä, M., Pettit, C. (eds) Intelligence for Future Cities. CUPUM 2023. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-031-31746-0_8
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