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Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips


A sustainable and robust stream of revenues is an essential element of the economic sustainability of bikeshare systems. To this effect, there is an unrelenting need to quantify and understand the impacts of pricing policies and operational considerations on a bikeshare system’s revenue and ridership. A notable gap exists in literature on studies related to the impact of changes in pricing policy on ridership and revenue. The primary objective of this research was to assess the impact of the introduction of a $2 per trip single-trip fare (STF) product for casual users by Capital Bikeshare (CaBi), the bikeshare system in the Washington DC metro area, on its ridership and revenue. Two-year ridership and revenue transaction datasets of CaBi were used in the analysis. A substantial data curation effort was undertaken to fuse elements between the two large transactional datasets. The effort not only facilitated the impact assessment but also enhanced the value of ridership dataset by identifying trips made by casual users by the type of fare product they purchased. The casual bikeshare user revenues were traced to individual bikeshare stations where trips originated, which allowed the comparison between revenues and ridership ‘before’ and ‘after’ the launch of STF at the station level. Over 22 million records on individual bikeshare trips and revenue transactions for 3 years and 330 bikeshare stations were analyzed. The results showed a statistically significant increase in casual user ridership after the introduction of STF. There was a statistically significant decrease in revenue per ride. Statistical tests indicated that these changes might be attributable to the introduction of STF. The methods used in this study are transferable. They can be used for curating ridership data and studying the impacts of bikeshare pricing policy changes on system usage and revenues at various public bikesharing systems with similar characteristics as Capital Bikeshare.

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This research was performed in cooperation with the District Department of Transportation (DDOT) and the Federal Highway Administration (FHWA). The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official view or policies of the FHWA or DDOT. This report does not constitute a standard, specification, or regulation. Also, partial funding for the research was provided by DDOT and the US Department of Transportation’s University Transportation Centers research program. The study team would like to express its gratitude to the panel, especially to Ms. Kimberley Lucas, Dr. Stefanie Brodie and Ms. Stephanie Dock of DDOT for their invaluable guidance, input and support.

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Correspondence to Mohan Venigalla.

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Venigalla, M., Kaviti, S. & Brennan, T. Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips. J. Big Data Anal. Transp. 2, 1–16 (2020).

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  • Bike share
  • Pricing
  • Revenue
  • Ridership
  • Data curation
  • Data fusion
  • Big data
  • Price sensitivity