Factors influencing the choice of shared bicycles and shared electric bikes in Beijing

By A. Campbell, Christopher R. Cherry, Megan S. Ryerson, Xinmiao Yang. 2016

456 citations recorded by the source corpus.

China leads the world in both public bikeshare and private electric bike (e-bike) growth. Current trajectories indicate the viability of deploying large-scale shared e-bike (e-bikeshare) systems in China. We employ a stated preference survey and multinomial logit to model the factors influencing the choice to switch from an existing transportation mode to bikeshare or e-bikeshare in Beijing. Demand is influenced by distinct sets of factors: the bikeshare choice is most sensitive to measures of effort and comfort while the e-bikeshare choice is more sensitive to user heterogeneities. Bikeshare demand is strongly negatively impacted by trip distance, temperature, precipitation, and poor air quality. User demographics however do not factor strongly on the bikeshare choice, indicating the mode will draw users from across the social spectrum. The e-bikeshare choice is much more tolerant of trip distance, high temperatures and poor air quality, though precipitation is also a highly negative factor. User demographics do play a significant role in e-bikeshare demand. Analysis of impact to the existing transportation system finds that both bikeshare and e-bikeshare will tend to draw users away from the "unsheltered modes", walk, bike, and e-bike. Although it is unclear if shared bikes are an attractive "first-and-last-mile solution", it is clear that e-bikeshare is attractive as a bus replacement. Language: en

Subfields: Automotive Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/b35b9a33334a1601dec71b0bf37fe1bdeb60c970

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In Automotive Engineering

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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): “…(2016) E-cargo bike Hess and Schubert (2019) Age EV Yoon et al. (2017) 18–30 years old (Wang and Yan 2016) Adult (Hu et al. 2018) E-bike Peak at 36 (Campbell et al. 2016) Age higher than 35 years old (Kaplan et al. 2018) E-cargo bike Hess and Schubert (2019) Education E-bike Campbell et al.…”
E-scooter sharing to serve short-distance transit trips: A Singapore case (2021, background): “The utility of choosing alternative i for individual n can be defined as: /$% = 0% + ;$% ∙ =$ + 2'($)*! ∙ >% + ?$% ∙ =$ + @% (7) where ?$% ∙ =$ is the individual-specific error component with ?$% as a vector of random terms of zero means and =$ as the observed variables of individual n, and @% is th”
Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China (2018, background): “Such a finding is in fact similar to the results of the aforementioned Beijing study (Campbell et al., 2016) in which the authors showed bike-sharing users could emerge across the social spectrum with no significant preference from any particular groups of people.”