Abstract
Since 2017, cities across the US have introduced shared e-scooter sharing programs that often co-exist with docked bikeshare services. Previous research has compared e-scooter and bikeshare service geographies and travel patterns, but few studies examine how shared e-scooter systems might substitute or supplement docked bikeshare trips. To fill the research gap, we treated the implementation of pilot shared e-scooter programs in the City of Chicago and the Boston metro area as quasi-natural experiments to assess how e-scooters influence docked bikeshare ridership. We obtained docked bikeshare trip data in each city and applied a difference-in-difference model with a propensity score matching method. Specifically, we investigated the effects of e-scooter sharing on docked bikeshare ridership in communities of concern, emphasizing the heterogeneous treatment effects and potential equity implications for developing micromobility systems. Results show that total micromobility trips--bikeshare plus shared e-scooters--in Chicago and Boston rose by 50% and 55%, respectively, during the pilot program. Despite the overall positive story for micromobility, shared e-scooters generally exerting negative effects on docked bikeshare ridership in both cities, with the exceptions of stations located in communities of concern, which experienced positive impacts on ridership. E-scooter pilots likewise yielded effects on how and who utilized bikeshare. Following shared e-scooter implementation, bikeshare trips lasting over 30 min increased in frequency. While the introduction of e-scooters reduced the total number of trips by both male and female subscribers, the number of trips made by male riders within communities of concern experienced an upswing. Results yield implications for policymakers seeking to increase access to micromobility services, particularly in communities of concern.
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Divvy system data: https://ride.divvybikes.com/system-data. Bluebikes system data: https://www.bluebikes.com/system-data. Neighborhood-level sociodemographic data: https://www.census.gov/programs-surveys/acs/data.html. Neighborhood-level built environment data: https://www.epa.gov/smartgrowth/smart-location-mapping#SLD. Employment data: https://lehd.ces.census.gov/. Transit data: https://gtfs.org/.
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SM: conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, writing—original draft, writing—reviewing & editing. AB: conceptualization, investigation, methodology, supervision, writing—reviewing & editing.
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Meng, S., Brown, A. How shared e-scooter programs affect docked bikeshare ridership in communities of concern: a tale of two cities. Transportation (2024). https://doi.org/10.1007/s11116-024-10473-w
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DOI: https://doi.org/10.1007/s11116-024-10473-w