How bikesharing changed destination distance for its users: A case study of Chicago Metropolitan Area

Shubhayan Ukil

University of Michigan-Ann Arbor

https://orcid.org/0000-0002-6443-9691

Aditi Misra

University of Colorado, Denver

https://orcid.org/0000-0002-5600-5973

DOI: https://doi.org/10.5198/jtlu.2025.2597

Keywords: Bikeshare, Travel distance, Quasi-Experimental, Destination, Causal Effect


Abstract

Shared bike use has been growing, especially post-pandemic, because it improves personal mobility and provides an alternative to walking while increasing connectivity to transit services. Existing research has examined the impact of these services on mode share and vehicle ownership. However, these services also hold the potential to influence the distance people travel to reach destinations. In this study, we examine the impact of Divvy shared bike services in the Chicago metropolitan region on the average trip distance of its users across all trips between 2008, when the service was not operational in the city, and 2018. We use repeated cross-sectional household travel datasets from 2008 and 2018 for analysis. We perform difference-in-difference regression to calculate the change in average trip distance for the shared bike user group. As there is no way to track people in repeated cross-sectional datasets, unlike a panel dataset, we use propensity score matching to match users between the two datasets. The results indicate that the average trip distance is reduced by 0.841 km (miles) for the shared bike user group with the presence of shared bike services. Shared bike users are more likely to live in urban areas where destinations are in proximity and use multi-modal travel, which could be a reason for this group’s reduced average trip distance. Given our findings, we recommend planning for shared bike services integrated with transit in urban areas and promoting mixed land use so that users can choose proximate destinations in dense urban areas. 


Author Biographies

Shubhayan Ukil, University of Michigan-Ann Arbor

Shubhayan UKil is a Doctoral Candidate in Urban and Regional Planning at the University of Michigan, Ann Arbor. His work looks at how the built environment and socioeconomic factors influence people’s travel and destination choices. He looks at travel decision-making as a complex phenomenon that needs to be analyzed by considering the impact of different mechanisms in the surrounding system. 

Aditi Misra, University of Colorado, Denver

Dr. Aditi Misra is an Assistant Professor in Transportation Engineering at the University of Colorado, Denver. My research has mainly been in the area of safety, travel behavior, and demand modeling with focus on marginalized road users. I have led multiple industry and government-sponsored projects in safe shared mobility within urban transportation systems. I and my group use statistical and econometric methods, emerging large-scale data as well as small-scale survey data in our research to understand how latent constructs like attitude and lifestyle influence decisions, choice patterns, and behaviors. 


References

Aman, J. J. C., Zakhem, M., & Smith-Colin, J. (2021). Towards equity in micromobility: Spatial analysis of access to bikes and scooters amongst disadvantaged populations. Sustainability, 13(21), 11856. https://doi.org/10.3390/su132111856

Angrist, J. D., & Pischke, J.-S. (2009). Differences-in-differences: Pre- and post-treatment and control. In Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University.

Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424. https://doi.org/10.1080/00273171.2011.568786

Bachand-Marleau, J., Lee, B. H. Y., & El-Geneidy, A. M. (2012). Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transportation Research Record, 2314(1), 66–71. https://doi.org/10.3141/2314-09

Beal, S., & Kupzyk, K. (2014). An introduction to propensity scores: What, when, and how. The Journal of Early Adolescence, 34, 66–92. https://doi.org/10.1177/0272431613503215

Becker, S. O., & Hvide, H. K. (2013). Do entrepreneurs matter? IZA Discussion Papers, No. 7146. Bonn, Germany: Institute for the Study of Labor.

Bieliński, T., Kwapisz, A., & Ważna, A. (2021). Electric bike-sharing services mode substitution for driving, public transit, and cycling. Transportation Research Part D: Transport and Environment, 96, 102883. https://doi.org/10.1016/j.trd.2021.102883

Chicago Metropolitan Agency for Planning. (2008).Travel tracker survey. Chicago: Chicago Metropolitan Agency for Planning.

Chicago Metropolitan Agency for Planning. (2019). My daily travel survey. Chicago: Chicago Metropolitan Agency for Planning.

Clark, C., Mokhtarian, P., Circella, G., & Watkins, K. (2019). User preferences for bicycle infrastructure in communities with emerging cycling cultures. Transportation Research Record, 2673(12), 89–102. https://doi.org/10.1177/0361198119854084

De Vos, J. (2018). Do people travel with their preferred travel mode? Analyzing the extent of travel mode dissonance and its effect on travel satisfaction. Transportation Research Part A: Policy and Practice, 117, 261–274. https://doi.org/10.1016/j.tra.2018.08.034

EPA. (2011). Smart Location Database (Version 1), Geodatabase. Retrieved from https://www.epa.gov/smartgrowth/smart-location-mapping#SLD

EPA. (2021). Smart Location Database (Version 3), Geodatabase. Retrieved from https://www.epa.gov/smartgrowth/smart-location-mapping#SLD

Faghih-Imani, A., & Eluru, N. (2015). Analyzing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system. Journal of Transport Geography, 44, 53–64.

Fishman, E. (2016). Bikeshare: A review of recent literature. Transport Reviews, 36(1), 92–113. https://doi.org/10.1080/01441647.2015.1033036

Fishman, E., Washington, S., & Haworth, N. (2014). Bike share’s impact on car use: Evidence from the United States, Great Britain, and Australia. Transportation Research Part D: Transport and Environment, 31, 13–20. https://doi.org/10.1016/j.trd.2014.05.013

Fukushige, T., Fitch, D. T., & Handy, S. (2023). Estimating vehicle-miles traveled reduced from dock-less e-bike-share: Evidence from Sacramento, California. Transportation Research Part D: Transport and Environment, 117, 103671. https://doi.org/10.1016/j.trd.2023.103671

Fukushige, T., Fitch, D. T., Mohiuddin, H., Andersen, H., & Jenn, A. (2022). Micromobility trip characteristics, transit connections, and COVID-19 effects (UC-ITS-2021-32). Berkeley, CA: The University of California Institute of Transportation Studies. https://escholarship.org/uc/item/2pk6t2cz

Griffin, G. P., & Sener, I. N. (2016). Planning for bike share connectivity to rail transit. Journal of Public Transportation, 19(2), 1–22. https://doi.org/10.5038/2375-0901.19.2.1

Huntington-Klein, N. (2022). Difference-in-differences. In The effect: An introduction to research design and causality. Boca Raton, FL: CRC Press, Chapman & Hall. https://theeffectbook.net/

Maat, K., & Timmermans, H. J. P. (2009). A causal model relating urban form with daily travel distance through activity/travel decisions. Transportation Planning and Technology, 32(2), 115–134. https://doi.org/10.1080/03081060902861285

Martin, R., & Xu, Y. (2022). Is tech-enhanced bikeshare a substitute or complement for public transit? Transportation Research Part A: Policy and Practice, 155, 63–78. https://doi.org/10.1016/j.tra.2021.11.007

Mix, R., Hurtubia, R., & Raveau, S. (2022). Optimal location of bike-sharing stations: A built environment and accessibility approach. Transportation Research Part A: Policy and Practice, 160, 126–142. https://doi.org/10.1016/j.tra.2022.03.022

Mohiuddin, H., Fitch, D., & Handy, S. (2022). Examining market segmentation to increase bike-share use: The case of the greater Sacramento region (NCST-UCD-RR-22-29). Davis, CA: Institute of Transportation Studies.

Oakes, J., & Johnson, P. J. (2006). Propensity score matching for social epidemiology, Vol. 1 (pp. 364–386). San Francisco: Jossey Bass-Wiley.

Reck, D. J., & Axhausen, K. W. (2021). Who uses shared micro-mobility services? Empirical evidence from Zurich, Switzerland. Transportation Research Part D: Transport and Environment, 94, 102803. https://doi.org/10.1016/j.trd.2021.102803

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41

Scheiner, J. (2010). Interrelations between travel mode choice and trip distance: Trends in Germany 1976–2002. Journal of Transport Geography, 18(1), 75–84. https://doi.org/10.1016/j.jtrangeo.2009.01.001

Shaheen, S. A., Martin, E. W., Cohen, A. P., & Finson, R. S. (2012). Public bikesharing in North America: Early operator and user understanding (MTI Report 11-26). San Jose, CA: Mineta Transportation Institute.

Singleton, P. (2017). Exploring the positive utility of travel and mode choice. Portland, OR: Portland State University. https://doi.org/10.15760/etd.5664

Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 25(1), 1–21. https://doi.org/10.1214/09-STS313

Stuart, E. A., Huskamp, H. A., Duckworth, K., Simmons, J., Song, Z., Chernew, M., & Barry, C. L. (2014). Using propensity scores in difference-in-differences models to estimate the effects of a policy change. Health Services & Outcomes Research Methodology, 14(4), 166–182. https://doi.org/10.1007/s10742-014-0123-z

Tatsuya Fukushige, D. F. (2021). Bike-share in the Sacramento region primarily substitutes for car and walking trips and reduces vehicle miles traveled. Berkeley, CA: University of California, Institute of Transportation Studies. https://escholarship.org/uc/item/18q404xb

U.S. Census Bureau. (2023) Longitudinal Employer-Household Dynamics (LODES 5). [dataset]. Retrieved from https://lehd.ces.census.gov/data/

Wooldridge, J. M. (2021). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. SSRN Electronic Journal. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3906345

Zhang, J., Pan, X., Li, M., & Yu, P. S. (2016). Bicycle-sharing system analysis and trip prediction. 2016 17th IEEE International Conference on Mobile Data Management (MDM), 174–179. https://doi.org/10.1109/MDM.2016.35

Zhong, H., Li, W., & Boarnet, M. G. (2021). A two-dimensional propensity score matching method for longitudinal quasi-experimental studies: A focus on travel behavior and the built environment. Environment and Planning B: Urban Analytics and City Science, 48(7), 2110–2122. https://doi.org/10.1177/2399808320982305

Zhou, Y., Yu, Y., Wang, Y., He, B., & Yang, L. (2023). Mode substitution and carbon emission impacts of electric bike sharing systems. Sustainable Cities and Society, 89, 104312. https://doi.org/10.1016/j.scs.2022.104312