Understanding the usage of dockless bike sharing in Singapore
By Yu Shen, Xiaohu Zhang, Jinhuan Zhao. 2018
414 citations recorded by the source corpus.
ABSTRACT A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.
Subfields: Automotive Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/e4bd9201c885641e3885c510514b61fa48106ff2
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Citations in the corpus
- Electric carsharing and micromobility: A literature review on their usage pattern, demand, and potential impacts (2020, background): “This range of electric shared micromobility trips overlaps with that of public transport and taxi modes (Guidon et al., 2019) and is also slightly higher than the typical trip length of shared bikes, which is about 1–1.6 km depending on the country (Boor, 2019; Shen et al., 2018).”
- Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C. (2020, background): “05 level once accounting for spatial dependencies (Shen et al., 2018).”
- Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model (2018, background): “However, the applicability of the OLS approach has been criticized for neglecting the spatial variations in metro-bikeshare usage [48].”