A bikeshare station area typology to forecast the station-level  ridership of system expansion

Steven R. Gehrke; Timothy F. Welch

doi:10.5198/jtlu.2019.1395

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Published

2019-04-11

Issue

Vol. 12 No. 1 (2019)

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Articles

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How to Cite

Gehrke, S. R., & Welch, T. F. (2019). A bikeshare station area typology to forecast the station-level ridership of system expansion. Journal of Transport and Land Use, 12(1). https://doi.org/10.5198/jtlu.2019.1395

A bikeshare station area typology to forecast the station-level ridership of system expansion

Steven R. Gehrke

Metropolitan Area Planning Council, Boston

http://orcid.org/0000-0001-9355-5571

Timothy F. Welch

Georgia Institute of Technology

DOI: https://doi.org/10.5198/jtlu.2019.1395

Keywords: bikeshare, built environment, latent class analysis, bike ridership, machine learning

Abstract

The continuous introduction and expansion of docked bikeshare systems with publicly available origin-destination data have opened exciting avenues for bikeshare research. In response, a flux of recent studies has examined the sociodemographic determinants and safety or natural environment deterrents of system ridership. An increasing abundance of disaggregate spatial data has also spurred recent calls for research aimed at extending the utility of these contextual data to model bikeshare demand and trip patterns. As planners and operators seek to expand bikeshare services into underserved areas, a need exists to provide a data-driven understanding of the spatial dynamics of bikeshare use. This study of the Washington, DC, metro region’s Capital Bikeshare (CaBi) program answers this call by performing a latent class cluster analysis to identify five bikeshare station area types based on variation in a set of land development pattern, urban design, and transportation infrastructure features. This typology is integrated into a planning application exploring the potential for system expansion into nearby jurisdictions and forecasting the associated trip-making potential between existing and proposed station locations.

References

Ahillen, M., Mateo-Babiano, D., & Corcoran, J. (2016). Dynamics of bike sharing in Washington, DC, and Brisbane, Australia: Implications for policy and planning. International Journal of Sustainable Transportation, 10(5), 441–454.

Bachand-Marleau, J., Lee, B. H. Y., & El-Geneidy, A. (2012). Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transportation Research Record: Journal of the Transportation Research Board, 2314, 66–71.

Beckman, J. D., & Goulias, K. G. (2008). Immigration, residential location, car ownership, and commuting behavior: A multivariate latent class analysis from California. Transportation, 35, 655–671.

Biernacki, C., Celeux, G., & Govaert, G. (1998). Assessing a mixture model for clustering with the integrated classification likelihood. (Report 3521). Rocquencourt, France: Institut national de recherche en informatique et en automatique (INRIA).

Buck, D., & Buehler, R. (2012). Bike lanes and other determinants of Capital Bikeshare trips. Paper presented at 91st Annual Meeting of the Transportation Research Board, Washington, DC.

Corcoran, J., & Li, T. (2014). Spatial analytical approaches in public bicycle sharing programs. Journal of Transport Geography, 41, 268–271.

DeMaio, P. (2009). Bike-sharing: History, impacts, models of provision, and future. Journal of Public Transportation, 12(4), 41–56.

El-Assi, W., Mahmoud, M. S., & Habib, K. N. (2017). Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation, 44(3), 589–613.

Faghih-Imani, A., Eluru, N., El-Geneidy, A., Rabbat, M., & Haq, U. (2014). How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306–314.

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.

Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.

Fraley, C., & Raftery, A. E. (2007). Model-based methods of classification: Using the mclust software in chemometrics. Journal of Statistical Software, 18(6), 1–13.

Halsey III, A. (2010, September 21). New bikeshare program provides wheels to casual cyclists in DC, Arlington. Washington Post. Retrieved from http://www.washingtonpost.com/wp-dyn/content/article/2010/09/20/AR2010092003815.html. Accessed March 13, 2018.

Higgins, C. D., & Kanaroglou, P. S. (2016). A latent class method for classifying and evaluating the performance of station area transit-oriented development in the Toronto region. Journal of Transport Geography, 52, 61–72.

Haughton, D., Legrand, P., & Woolford, S. (2009). Review of three latent class cluster analysis packages: Latent Gold, poLCA, and MCLUST. The American Statistician, 63(1), 81–91.

Krykewycz, G., Puchalsky, C. Rocks, J. Bonnette, B., & Jaskiewicz, F. (2010). Defining a primary market and estimating demand for major bicycle-sharing program in Philadelphia, Pennsylvania. Transportation Research Record: Journal of the Transportation Research Board, 2143, 117–124.

Ma, T., Liu, C., & Erdogan, S. (2015). Bicycle sharing and public transit: Does Capital Bikeshare affect Metrorail ridership in Washington, DC? Transportation Research Record: Journal of the Transportation Research Board, 2534, 1–9.

National Association of City Transportation Officials. (2016). Bike share station siting guide. Retrieved from http://nacto.org/wp-content/uploads/2016/04/NACTO-Bike-Share-Siting-Guide_FINAL.pdf

O’Brien, O., Cheshire, J., & Batty, M. (2014). Mining bicycle sharing data for generating insights into sustainable transport systems. Journal of Transport Geography, 34, 262–273.

R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org

Rixey, R. A. (2013). Station-level forecasting of bikesharing ridership: Station network effects in three U.S. Systems. Transportation Research Record: Journal of the Transportation Research Board, 2387, 46–55.

Shaheen, S. A., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia: Past, present, and future. Transportation Research Record: Journal of the Transportation Research Board, 2143, 159–167.

Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J.A. Hagenaars & A. L. McCutcheon (Eds.) Applied latent class analysis (pp. 89–106). Cambridge: Cambridge University Press.

Wang, X., Lindsey, G. Schoner, J., & Harrison, A. (2016). Modeling bike share station activity: Effects of nearby businesses and jobs on trips to and from stations. Journal of Urban Planning and Development, 142(1), 1–9.

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