As children, we were taught sharing is caring. Turns out it’s also good for business. Opportunities abound to monetize goods and services through joint use: Share your apartment with strangers through room rental services like Airbnb. Turn your car into a taxi service. Wait in line for people who are willing to pay to avoid queueing themselves.
Bike sharing services that increasingly are popping up in cities across the country and around the world are yet another manifestation of this new economy. At designated stands throughout these cities, riders can pick up and drop off bikes for a leisurely spin or a weekday commute. The service has the potential to change the ways that people navigate cities, diverting users from public transit or automobiles. There are potential environmental and health impacts as well — bicycles are a form of sustainable transportation, and riders might benefit from the physical activity, though bike shares often rely on riders to supply their own helmets, which poses its own challenges and risks.
As bike sharing grows in popularity and prevalence, researchers have begun to study its impacts. In recent years a body of academic scholarship has emerged that examines the health effects of bike sharing, distribution of access to these services, use of helmets and more. This roundup features research on these issues and general reviews of research on bike sharing.
“Bikeshare: A Review of Recent Literature”
Fishman, Elliot. Transport Reviews, 2016. DOI: 10.1080/01441647.2015.1033036.
Abstract: “The number of cities offering bikeshare has increased rapidly, from just a handful in the late 1990s to over 800 currently. This paper provides a review of recent bikeshare literature. Several themes have begun to emerge from studies examining bikeshare. Convenience is the major motivator for bikeshare use. Financial savings has been found to motivate those on a low income and the distance one lives from a docking station is an important predictor for bikeshare membership. In a range of countries, it has been found that just under 50 percent of bikeshare members use the system less than once a month. Men use bikeshare more than women, but the imbalance is not as dramatic as private bike riding (at least in low cycling countries). Commuting is the most common trip purpose for annual members. Users are less likely than private cyclists to wear helmets, but in countries with mandatory helmet legislation, usage levels have suffered. Bikeshare users appear less likely to be injured than private bike riders. Future directions include integration with e-bikes, GPS (global positioning system), dockless systems and improved public transport integration. Greater research is required to quantify the impacts of bikeshare, in terms of mode choice, emissions, congestion and health.”
“Bike Sharing: A Review of Evidence on Impacts and Processes of Implementation and Operation”
Ricci, Miriam. Research in Transportation Business & Management, 2015. DOI: 10.1016/j.rtbm.2015.03.003.
Abstract: “Despite the popularity of bike sharing, there is a lack of evidence on existing schemes and whether they achieved their objectives. This paper is concerned with identifying and critically interpreting the available evidence on bike sharing to date, on both impacts and processes of implementation and operation. The existing evidence suggests that bike sharing can increase cycling levels but needs complementary pro-cycling measures and wider support to sustainable urban mobility to thrive. Whilst predominantly enabling commuting, bike sharing allows users to undertake other key economic, social and leisure activities. Benefits include improved health, increased transport choice and convenience, reduced travel times and costs, and improved travel experience. These benefits are unequally distributed, since users are typically male, younger and in more advantaged socio-economic positions than average. There is no evidence that bike sharing significantly reduces traffic congestion, carbon emissions and pollution. From a process perspective, bike sharing can be delivered through multiple governance models. A key challenge to operation is network rebalancing, while facilitating factors include partnership working and inclusive scheme promotion. The paper suggests directions for future research and concludes that high-quality monitoring impact/process data, systematically and consistently collected, as well as innovative and inclusive evaluation methods are needed.”
“Public Bikesharing in North America: Early Operator Understanding and Emerging Trends”
Shaheen, Susan; Cohen, Adam; Martin, Elliot. Transportation Research Record, 2014. DOI: 10.3141/2387-10.
Abstract: “Public bikesharing, the shared use of a bicycle fleet by the public, is an innovative mobility strategy that has emerged recently in major North American cities. Typically, bikesharing systems position bicycles throughout an urban environment, within a network of docking stations, for immediate access. Bikesharing services with a basis in information technology (IT) began to emerge in North America approximately 5 years ago. Twenty-eight IT-based programs were deployed between 2007 and March 2013. Twenty-four are operational, two are temporarily suspended, and two are now defunct. This study examined the growth potential of bikesharing in North America on the basis of a survey of all 15 IT-based public bikesharing systems in operation in the United States and all four programs deployed in Canada as of January 2012. These programs accounted for 172,070 users and 5,238 bicycles in the United States and for 44,352 users and 6,235 bicycles in Canada. Early operator understanding of North American public bikesharing is reviewed and emerging trends for prospective program start-ups are discussed.”
“Health Effects of the London Bicycle Sharing System: Health Impact Modelling Study”
Woodcock, J.; et al. BMJ, 2014. DOI: 10.1136/bmj.g425.
Findings: “London’s bicycle sharing system has positive health impacts overall, but these benefits are clearer for men than for women and for older users than for younger users. The potential benefits of cycling may not currently apply to all groups in all settings.”
“Public Bicycle Share Programs and Head Injuries”
Graves, Janessa M.; et al. American Journal of Public Health, 2014. DOI: 10.2105/AJPH.2014.302012.
Results: “In public bicycle share program (PBSP) cities, the proportion of head injuries among bicycle-related injuries increased from 42.3 percent before PBSP implementation to 50.1 percent after (P < 0.01). This proportion in comparison cities remained similar before (38.2 percent) and after (35.9 percent) implementation (P = 0.23). Odds ratios for head injury were 1.30 (95 percent confidence interval = 1.13, 1.67) in PBSP cities and 0.94 (95 percent confidence interval = 0.79, 1.11) in control cities (adjusted for age and city) when we compared the period after implementation to the period before.”
“Helmet-Wearing Practices and Barriers in Toronto Bike-Share Users: A Case-Control Study”
Friedman, Steven Marc; et al. Canadian Journal of Emergency Medicine, 2016. DOI: 10.1017/cem.2015.22.
Findings: “PBSP users surveyed appear to make deliberate decisions regarding helmet use. Non helmet wearers tended to be male, slightly younger, and less likely to use helmets on their personal bikes. As Toronto cyclists who do not wear helmets on PBSP generally do not wear helmets on their personal bikes, interventions to increase helmet use should target both personal and bike-share users. Legislating helmet use and provision of rental helmets could improve helmet use among bike-share users, but our results suggest some risk of reduced cycling with legislation.”
“Public Bike Sharing in New York City: Helmet Use Behavior Patterns at 25 Citi BikeTM Stations”
Basch, Corey H.; et al. Journal of Community Health, 2015. DOI: 10.1007/s10900-014-9967-y.
Abstract: “Urban public bicycle sharing programs are on the rise in the United States. Launched in 2013, NYC’s public bicycle share program, Citi Bike™ is the fastest growing program of its kind in the nation, with nearly 100,000 members and more than 330 docking stations across Manhattan and Brooklyn. The purpose of this study was to assess helmet use behavior among Citi Bike™ riders at 25 of the busiest docking stations. The 25 Citi Bike™ Stations varied greatly in terms of usage: total number of cyclists (N = 96-342), commute versus recreation (22.9-79.5 percent commute time riders), weekday versus weekend (6.0-49.0 percent weekend riders). Helmet use ranged between 2.9 and 29.2 percent across sites (median = 7.5 percent). A total of 4,919 cyclists were observed, of whom 545 (11.1 percent) were wearing helmets. Incoming cyclists were more likely to wear helmets than outgoing cyclists (11.0 vs 5.9 percent, p = 0.000). NYC’s bike share program endorses helmet use, but relies on education to encourage it. Our data confirm that, to date, this strategy has not been successful.”
“Prevalence of Bicycle Helmet Use by Users of Public Bikeshare Programs”
Fischer, Christopher M.; et al. Annals of Emergency Medicine, 2012. DOI: 10.1016/j.annemergmed.2012.03.018.
Findings: “There were 43 observation periods in 2 cities at 36 locations; 3,073 bicyclists were observed. There were 562 (18.3 percent; 95 percent confidence interval [CI] 16.4 percent to 20.3 percent) bicyclists riding shared bicycles. Overall, 54.5 percent of riders were unhelmeted (95 percent CI 52.7 percent to 56.3 percent), although helmet use varied significantly with sex, day of use, and type of bicycle. Bikeshare users were unhelmeted at a higher rate compared with users of personal bicycles (80.8 percent versus 48.6 percent; 95 percent CI 77.3 percent to 83.8 percent versus 46.7 percent to 50.6 percent). Logistic regression, controlling for type of bicycle, sex, day of week, and city, demonstrated that bikeshare users had higher odds of riding unhelmeted (odds ratio [OR] 4.4; 95 percent CI 3.5 to 5.5). Men had higher odds of riding unhelmeted (OR 1.6; 95 percent CI 1.4 to 1.9), as did weekend riders (OR 1.3; 95 percent CI 1.1 to 1.6).”
“Helmet Wearing Among Users of a Public Bicycle-Sharing Program in the District of Columbia and Comparable Riders on Personal Bicycles”
Kraemer, John D.; Roffenbender, Jason S.; Anderko, Laura. American Journal of Public Health, 2012. DOI: 10.2105/AJPH.2012.300794.
Findings: “Helmet use was significantly less common among Bikeshare users than comparable cyclists on their own bicycles. Adjusting for potential confounders, persons observed using Capital Bikeshare at times and locations consistent with being a daily commuter had one fifth the odds of helmet use (odds ratio [OR] = 0.200; 95 percent confidence interval [CI] = 0.110, 0.362) of those observed at the same time and locations on private bicycles. Similarly, among persons classified as casual riders, users of Capital Bikeshare had less than one tenth the odds of helmet use (OR = 0.090; 95 percent CI = 0.061, 0.132; Table 2).”
“First/Last Mile Transit Access as an Equity Planning Issue”
Boarnet, Marlon G., et al. Transportation Research Part A: Policy and Practice, 2017. DOI: 10.1016/j.tra.2017.06.011.
Abstract: “Previous studies have established that residents of low-income neighborhoods in major metropolitan areas have access to many more jobs by car than by transit. In this paper, we revisit this question and present evidence on how the mode of transit station access/egress (by walking, bicycling, or driving) can importantly influence the gap between car and transit accessibility in the San Diego region. We construct two accessibility measures to analyze low-wage job access by transit: (1) the number of low-wage jobs accessible within a 30-min commute and (2) the number of low-wage jobs within a 30-min commute adjusted by the number of potentially competing workers who live within 30 min. We then simulate several policy changes that could reduce the difference in transit vs car accessibility. Examples include using faster station access/egress modes such as bicycling and driving to or from transit stations and reducing transit service wait time. Our results demonstrate that in the San Diego region, if transit riders walk to/from transit stops, low-wage job accessibility by car is almost 30 times larger than low-wage job accessibility by public transit. We find that changing the mode of access and egress to and from stations is more effective at improving transit access to low-wage jobs than policies that reduce transit wait time or improve service headway. Given the transition of transportation to a ‘service’ or ‘sharing’ economy, these results have important implications for how to improve access to employment in low income neighborhoods.”
“Bringing Bike Share to a Low-Income Community: Lessons Learned Through Community Engagement, Minneapolis, Minnesota, 2011”
Kretman Stewart, Sarah; Johnson, David C.; Smith, William P. Preventing Chronic Disease, 2013. DOI: 10.5888/pcd10.120274.
Abstract: “… The Minneapolis Health Department funded the Nice Ride Minnesota bike share system to expand to the Near North community in Minneapolis, Minnesota. Near North is a diverse, low-income area of the city where residents experience health disparities, including disparities in physical activity levels. … Results show that the first season of the expansion was moderately successful in improving outreach efforts and adapting bike share to meet the needs of low-income populations. However, environmental change without adequate, ongoing community engagement may not be sufficient to result in behavior change.”
“Breaking Barriers to Bike Share: Insights from Residents of Traditionally Underserved Neighborhoods”
McNeil, Nathan; et al. Transportation Research and Education Center report, 2017. DOI: 10.15760/trec.176.
Abstract: “Evidence has shown that higher income and white populations are overrepresented in both access to and use of bike share. Efforts to overcome underserved communities’ barriers to access and use of bike share have been initiated in a number of cities, including those working with the Better Bike Share Partnership (BBSP) to launch and test potentially replicable approaches to improve the equity outcomes. This report describes findings from a survey of residents living near bike share stations placed in underserved communities of select BBSP cities: Philadelphia, Chicago, and Brooklyn.”
“Bikeshare Use in Urban Communities: Individual and Neighborhood Factors”
Oates, Gabriela R., et al. Ethnicity & Disease, 2017. DOI: 10.18865/ed.27.S1.303.
Abstract: “… 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. … 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.”