Abstract
The bike-sharing system (BSS) is an emerging travel mode that has attracted increased attention in recent years. One of the most critical reasons for this increased attention is that the BSS claims to solve the first-mile and last-mile problems, and can be used to connect with existing transit. However, some studies suggest that BSSs could compete with transit rather than collaborating. Previous studies only focused on large-sized BSSs, ignoring an analysis of the impact of small-sized BSSs. To fill this gap, this paper conducted a case study to investigate the impacts of introducing a small-sized BSS on transit (including regular bus, express, and streetcar) usage in Tucson, Arizona. All transit routes are categorized into two groups: treated routes with the defined buffer of BSS and control routes without BSS. Then, the synthetic control method (SCM) is employed to provide an unbiased comparison on the average ridership per stop of the treated transit routes. The ridership data and point-of-interest data are collected and used to synthesize virtual treatment transit routes. The results show that a small-sized BSS generally has a slight impact on the ridership of most transit routes because of the limited coverage. However, the streetcar experiences an increase in ridership and increases by 0.55 passengers as a result of 1 BSS trip. Furthermore, the relationship between a small-sized BSS and transit may be also dependent on whether a transit route can access areas having the densest BSS network. These findings suggest that the role of BSSs in an urban transportation system can be controlled by relocating the locations of BSS stations considering the characteristics of transit routes.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abadie A (2005) Semiparametric difference-in-differences estimators. Rev Econ Stud 72:1–19. https://doi.org/10.1111/0034-6527.00321
Abadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. J Am Stat Assoc 105:493–505. https://doi.org/10.1198/jasa.2009.ap08746
Bachand-Marleau J, Lee BHY, El-Geneidy AM (2012) Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transp Res Rec 2314:66–71. https://doi.org/10.3141/2314-09
Bohn S, Lofstrom M, Raphael S (2013) Did the 2007 legal Arizona workers act reduce the state’s unauthorized immigrant population? Rev Econ Stat 96:258–269. https://doi.org/10.1162/REST_a_00429
Campbell KB, Brakewood C (2017) Sharing riders: how bikesharing impacts bus ridership in New York City. Transp Res Part A Policy Pract 100:264–282. https://doi.org/10.1016/j.tra.2017.04.017
Caulfield B, O’Mahony M, Brazil W, Weldon P (2017) Examining usage patterns of a bike-sharing scheme in a medium sized city. Transp Res Part A Policy Pract 100:152–161. https://doi.org/10.1016/j.tra.2017.04.023
DeMaio P (2009) Bike-sharing: history, impacts, models of provision, and future. J Public Transp 12(4):41–56. https://doi.org/10.5038/2375-0901.12.4.3
DeMaio P, Meddin R (2019) The Bike-sharing World Map [WWW Document]. Bike Share Map. http://www.bikesharemap.com (Accessed 26 July 2019).
Faghih-Imani A, Eluru N (2015) Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system. J Transp Geogr 44:53–64. https://doi.org/10.1016/j.jtrangeo.2015.03.005
Faghih-Imani A, Eluru N, El-Geneidy AM, Rabbat M, Haq U (2014) How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal. J Transp Geogr 41:306–314. https://doi.org/10.1016/j.jtrangeo.2014.01.013
Gebhart K, Noland RB (2014) The impact of weather conditions on bikeshare trips in Washington, DC. Transportation 41:1205–1225. https://doi.org/10.1007/s11116-014-9540-7
Gordon-Koven L, Levenson N (2014) Citi bike takes New York. Rudin Center for Transportation, New York
Li W, Kamargianni M (2018) 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. Transp Res Part A Policy Pract 111:277–291. https://doi.org/10.1016/j.tra.2018.01.019
Li X, Cottam A, Wu Y-J, Khani A (2020) Can a bikesharing system reduce fuel consumption? Case study in Tucson, Arizona. Transp Res Part D Transp Environ 89:102604. https://doi.org/10.1016/j.trd.2020.102604
Ma X, Zhang X, Li X, Wang X, Zhao X (2019) Impacts of free-floating bikesharing system on public transit ridership. Transp Res Part D Transp Environ 76:100–110. https://doi.org/10.1016/j.trd.2019.09.014
Martin EW, Shaheen SA (2014) Evaluating public transit modal shift dynamics in response to bikesharing: a tale of two U.S. cities. J Transp Geogr 41:315–324. https://doi.org/10.1016/j.jtrangeo.2014.06.026
McKenzie B (2014) Modes less traveled—bicycling and walking to work in the United States: 2008–2012 (No. ACS-25). US Department of Commerce, Economics and Statistics Administration, US Census Bureau, Washington, DC
Munasib A, Rickman DS (2015) Regional economic impacts of the shale gas and tight oil boom: a synthetic control analysis. Reg Sci Urban Econ 50:1–17. https://doi.org/10.1016/j.regsciurbeco.2014.10.006
National Association of City Transportation Officials (NACTO) (2018) Shared micromobility in the U.S.: 2018
Nikitas A (2018) Understanding bike-sharing acceptability and expected usage patterns in the context of a small city novel to the concept: a story of ‘Greek Drama.’ Transport Res F Traffic Psychol Behav 56:306–321. https://doi.org/10.1016/j.trf.2018.04.022
Noh H, Kramer E, Sun A (2019) Development of strategic regional employment data assessment using Google places API. Transp Res Rec 2673:254–263. https://doi.org/10.1177/0361198119852068
Noland RB, Smart MJ, Guo Z (2016) Bikeshare trip generation in New York City. Transp Res Part A Policy Pract 94:164–181. https://doi.org/10.1016/j.tra.2016.08.030
Saberi M, Ghamami M, Gu Y, Shojaei MH, Fishman E (2018) Understanding the impacts of a public transit disruption on bicycle sharing mobility patterns: a case of Tube strike in London. J Transp Geogr 66:154–166. https://doi.org/10.1016/j.jtrangeo.2017.11.018
Shaheen S, Martin E, Cohen A (2013) Public bikesharing and modal shift behavior: a comparative study of early bikesharing systems in North America. Int J Transp 1:35–54. https://doi.org/10.14257/ijt.2013.1.1.03
Sun F, Chen P, Jiao J (2018) Promoting public bike-sharing: a lesson from the unsuccessful Pronto system. Transp Res Part D Transp Environ 63:533–547. https://doi.org/10.1016/j.trd.2018.06.021
Walker J (2012) Human transit: how clearer thinking about public transit can enrich our communities and our lives. Island Press, Washington
Xu Y, Chen D, Zhang X, Tu W, Chen Y, Shen Y, Ratti C (2019) Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system. Comput Environ Urban Syst 75:184–203. https://doi.org/10.1016/j.compenvurbsys.2019.02.002
Acknowledgements
The authors would like to thank the City of Tucson for funding and data support. We would also like to thank Jennifer Toothaker, Francisco Leyva, Andrew Bemis, Kim Okada, and Hyunsoo Noh for providing valuable advice and technical support in this project. Special thanks to Adrian Cottam and Lilly Cottam for their assistance in English proofreading.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, X., Wu, YJ. & Khani, A. Investigating a small-sized bike-sharing system’s impact on transit usage: a synthetic control analysis in Tucson, Arizona. Public Transp 14, 441–458 (2022). https://doi.org/10.1007/s12469-021-00278-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12469-021-00278-w