Stephen J Mooney1,2, Kate Hosford3,4, Bill Howe5, An Yan5, Meghan Winters3, Alon Bassok6, Jana A Hirsch7,8. 1. Department of Epidemiology, University of Washington, Seattle, WA. 2. Harborview Injury Prevention and Research Center, University of Washington, Seattle, WA. 3. Faculty of Health Sciences, Simon Fraser University, Burnaby, BC. 4. Centre for Hip Health and Mobility, University of British Columbia, Vancouver, BC. 5. The Information School, University of Washington, Seattle, WA. 6. Washington State Transportation Center, University of Washington, Seattle. 7. Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA. 8. Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA.
Abstract
BACKGROUND: Bike sharing systems have potential to substantially boost active transportation levels (and consequent physical and mental health) in urban populations. We explored equity of spatial access in a novel 'dockless' bike share system that does not that constrain bike pickup and drop-off locations to docking stations. METHODS: Starting in July 2017, Seattle, Washington piloted a dockless bike share system that made 10,000 bikes available. We merged data on resident sociodemographic and economic characteristics from the American Community Survey about 93 defined neighborhoods with data about bike locations, bike idle time, and which neighborhoods operators rebalanced bikes to. We used mapping and descriptive statistics to compare access between neighborhoods along sociodemographic and economic lines. RESULTS: With many bikes available, no neighborhood was consistently excluded from access. However, the average availability ranged from 3 bikes per day to 341 per day. Neighborhoods with more bikes had more college-educated residents (median 75% college-educated vs. 65%) and local community resources (median opportunity index score of 24 vs. 19), and higher incomes (median 83,202 vs. 71,296). Rebalancing destinations were strongly correlated with neighborhood demand (r=0.61). CONCLUSIONS: The overall scale of the dockless system ensured there was baseline access throughout Seattle. We observed modest inequities in access along sociodemographic lines, similar to prior findings in studies of docked bike share systems. Dockless bike share systems hold promise for offering equitable spatial access to bike sharing.
BACKGROUND: Bike sharing systems have potential to substantially boost active transportation levels (and consequent physical and mental health) in urban populations. We explored equity of spatial access in a novel 'dockless' bike share system that does not that constrain bike pickup and drop-off locations to docking stations. METHODS: Starting in July 2017, Seattle, Washington piloted a dockless bike share system that made 10,000 bikes available. We merged data on resident sociodemographic and economic characteristics from the American Community Survey about 93 defined neighborhoods with data about bike locations, bike idle time, and which neighborhoods operators rebalanced bikes to. We used mapping and descriptive statistics to compare access between neighborhoods along sociodemographic and economic lines. RESULTS: With many bikes available, no neighborhood was consistently excluded from access. However, the average availability ranged from 3 bikes per day to 341 per day. Neighborhoods with more bikes had more college-educated residents (median 75% college-educated vs. 65%) and local community resources (median opportunity index score of 24 vs. 19), and higher incomes (median 83,202 vs. 71,296). Rebalancing destinations were strongly correlated with neighborhood demand (r=0.61). CONCLUSIONS: The overall scale of the dockless system ensured there was baseline access throughout Seattle. We observed modest inequities in access along sociodemographic lines, similar to prior findings in studies of docked bike share systems. Dockless bike share systems hold promise for offering equitable spatial access to bike sharing.
Authors: Stephen J Mooney; Daniel M Sheehan; Garazi Zulaika; Andrew G Rundle; Kevin McGill; Melika R Behrooz; Gina Schellenbaum Lovasi Journal: Am J Public Health Date: 2016-02-18 Impact factor: 9.308
Authors: Stephen J Mooney; Caroline Magee; Kolena Dang; Julie C Leonard; Jingzhen Yang; Frederick P Rivara; Beth E Ebel; Ali Rowhani-Rahbar; D Alex Quistberg Journal: Am J Epidemiol Date: 2018-09-01 Impact factor: 4.897
Authors: Jasper Schipperijn; Jacqueline Kerr; Scott Duncan; Thomas Madsen; Charlotte Demant Klinker; Jens Troelsen Journal: Front Public Health Date: 2014-03-10
Authors: Bill Howe; Jackson Maxfield Brown; Bin Han; Bernease Herman; Nic Weber; An Yan; Sean Yang; Yiwei Yang Journal: Eur Phys J Spec Top Date: 2022-04-09 Impact factor: 2.891