A brief study exploring social equity within bicycle share programs

Gavin, Kristin; Bennett, Aronté; Auchincloss, Amy H.; Katenta, Anna

doi:10.1080/19427867.2015.1126065

Cited by 30 publications

(15 citation statements)

References 12 publications

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“…Previous studies have reported that bikeshare users tend to be younger, educated, and affluent Caucasian males. [30][31][32][33][34][35] Disparities by race have been reported, with African Americans being less represented as membership holders. 30 Studies from the United Kingdom also document that residents of deprived areas are underrepresented as bikeshare users.…”

Section: Discussionmentioning

confidence: 99%

Bikeshare Use in Urban Communities: Individual and Neighborhood Factors

et al. 2017

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Objective: Bicycling is an affordable way to increase access to employment, schooling, and services and an effective measure against obesity. Bikeshare programs can make bicycling accessible to diverse populations, but little evidence exists on their adoption in low-resource neighborhoods. Our study examined factors associated with bikeshare use in a metropolitan area in the southern United States.Methods: We performed a retrospective cross-sectional analysis of a database of clients (N=815) who rented a bicycle from Zyp Bikeshare in Birmingham, Alabama between October 2015 and November 2016. Individual-level variables included bike use frequency, average speed, total miles traveled, total minutes ridden, bike type (traditional vs electricity-assisted pedelec), membership type, sex, and age. Area-level data aggregated to Census tracts, proxies for neighborhoods, were obtained from the 2010 US Census after geocoding clients’ billing addresses. Using exploratory factor analysis, a neighborhood socioeconomic disadvantage index (SDI) was constructed. Bikeshare station presence in a tract was included as a covariate. Multivariate linear regression models, adjusted for clustering on Census tracts, were estimated to determine predictors of bikeshare use.Results: In a multivariate regression model of individual and neighborhood characteristics adjusted for clustering, each decile increase in the SDI was associated with a 9% increase in bikeshare use (P<.001). Bikeshare use was also positively associated with speed (.1, P<.001), total miles (.008, P<.001), and pedelec use (1.02, P<.01).Conclusion: Higher neighborhood socioeconomic disadvantage is associated with higher bikeshare use. Bikeshare is a viable transportation option in low-resource neighborhoods and may be an effective tool to improve the connectivity, livability, and health of urban communities. Ethn Dis. 2017;27(Suppl 1):303-312; doi:10.18865/ed.27.S1.303.

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Section: Discussionmentioning

confidence: 99%

Bikeshare Use in Urban Communities: Individual and Neighborhood Factors

et al. 2017

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“…Convenience(Joo 2017), satisfaction, and car availability also have an important influence on fixed-station car sharing, since the user needs to reach a fixed parking location before and after use.User characteristicsThe user characteristics (social demographic variables, including cost savings, age, gender, and educational attainment) have different effects on bike sharing, ride sharing, and fixed-station car sharing. The positive impact of income on shared transportation has been widely recognized by scholars(Efthymiou et al 2013;Gavin et al 2016;. The impact of cost savings on ridesharing and short-term car rental is controversial Nielsen et al (2015).…”

mentioning

confidence: 99%

Travel behaviours, user characteristics, and social-economic impacts of shared transportation: a comprehensive review

Sun

Wang

Zhou

et al. 2019

International Journal of Logistics Research and Applications

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Shared transportation is playing an increasingly important role in sustainable urban transportation planning and control. Because it significantly affects people's daily life, socioeconomic development, and the environment, shared transportation has attracted attention from scholars and practitioners alike. For the former, the large number of articles published on the topic reveals the growing interest. Of interest are the articles that focus on travel behaviours, user characteristics, and social-economic impacts of shared transportation. Herein, we review 356 peer-reviewed articles on the topic that were published between January 2003 and September 2017. We employ a bibliometric method to investigate the overall characteristics, research methodology, research highlights, and research areas of these articles. Our analysis explores and discusses user travel behaviours, traffic satisfaction, key determinants, impact, development planning, and policies. Finally, we provide a detailed discussion on the future research challenges and new research directions for shared transportation.

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“…The first approach is to analyze sociodemographic profiles of registered users, e.g., Goodman and Cheshire (2014); Wang and Akar (2019) sharing service areas (defined as a buffer around a bike-sharing station), e.g., Hosford and Winters (2018), Ursaki and Aultman-Hall (2015). Another popular approach involves a comparison of sociodemographic characteristics of registered users with those of census residences within bikesharing service areas, e.g., Gavin et al (2016), Ogilvie and Goodman (2012).…”

Section: Review Of Studies On Bike-sharing Equitymentioning

confidence: 99%

“…As an improvement over existing bike-sharing equity analyses, we consider all three of these steps in our measure of accessibility, rather than assuming that only the population residing within the service area of a bike-sharing facility can access bike-sharing (Gavin et al, 2016). This allows capture of the impact of distance to a bike-sharing facility and travel distance of individual trips on accessibility.…”

Section: Bike-sharing Accessibility For a Single Tripmentioning

confidence: 99%

“…PlanHillsborough (2017), respectively. For sociodemographic attributes, we consider age (18-35, 35-45, 45-65, above 65), gender (male, female), household income level (below poverty, middle income defined as above the 2009 poverty level but with an annual household income below $75,000, and upper income with an annual household income above $75,000), race (white, black, Asian, other), and ethnicity (Hispanic and non-Hispanic), after Gurram (2019) and Gavin et al (2016).…”

Section: Data Sourcesmentioning

confidence: 99%

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